DE102015221092A1 - Method and information system for detecting at least one plant parameter data set of a plant growing on a field - Google Patents

Abstract

The invention relates to a method (300) for detecting at least one plant parameter data set (105) of a plant (115a, 115b) growing on a field (110). The method (300) comprises a step of reading in (310) position information (130) of the plant (115a, 115b), the position information (130) representing a geographical position of the plant (115a, 115b). Furthermore, the method (300) comprises a step of determining (320) growth information (155) of the plant (115a, 115b) representing a growth or a state of development of the plant (115a, 115b). Finally, the method (300) comprises a step of storing (330) the position information (130) of the plant (115a, 115b) and the growth information (155) of the plant (115a, 115b) or information derived from the growth information (155) ( 160) as a plant parameter data set (105) to capture the plant parameter data set (105).

Description

State of the art

The invention is based on a method and an information system according to the preamble of the independent claims. The subject of the present invention is also a computer program.

In agriculture, largely standardized methods for assessing breeding or treatment progress are used in the breeding of new varieties and in the optimization of seed quality. These procedures are used in extensive series of experiments involving multiple repetitions in different locations.

The highest informative value for breeders have test series (so-called series), which take place in the field and largely reflect the actual growing situation. For this field trial, therefore, several large-sized beds are created, which are divided into plots.

For the most part, field tests use buffing methods in which the characteristics of a plant to be assessed (germination, sprouting, stature height, leaf size, flowering, fruit ripening, etc.) are assigned a credit score (numerical value from 1 to 9) according to their stage of development. The characteristics to be considered depend on the type of crop. The assignment of the credit note numbers is done by human observers. The results are usually noted in the field on paper and transferred in a further processing step in a PC. In rare cases, today handheld computers (tablets) are used with specific input masks for the assessment of certain cultures and characteristics.

The data collected by persons in the field trial according to the rating scheme will be evaluated afterwards. The evaluation is usually a manual procedure in which the partial results are summarized in tables and then analyzed by means of statistical methods.

Disclosure of the invention

Against this background, the method presented here, an information system that uses this method, and finally a corresponding computer program according to the main claims are presented. The measures listed in the dependent claims advantageous refinements and improvements of the independent claim device are possible.

• – Einlesen einer Positionsinformation der Pflanze, wobei die Positionsinformation eine geografische Position der Pflanze repräsentiert;
• – Ermitteln einer Wuchsinformation der Pflanze, die ein Wachstum oder einen Entwicklungszustand der Pflanze repräsentiert; und
• – Speichern der Positionsinformation der Pflanze und der Wuchsinformation der Pflanze oder einer von der Wuchsinformation abgeleiteten Information als Pflanzenparameterdatensatz, um den Pflanzenparameterdatensatz zu erfassen.

The approach presented here provides a method for acquiring at least one plant parameter data set of a plant growing on a field, the method comprising the following steps:

• - reading position information of the plant, the position information representing a geographical position of the plant;
• Ascertaining growth information of the plant, which represents a growth or a state of development of the plant; and
• Storing the position information of the plant and the growth information of the plant or of information derived from the growth information as a plant parameter data record in order to record the plant parameter data record.

A plant parameter data record can be understood as a data record in which several parameters or information are contained. Positional information may, for example, be understood to be a geographical position of the plant in a field or a relative position of the plant in a field or cultivation area, for example with respect to a corner coordinate of the field. By way of example, growth information can be understood as meaning a parameter and / or image data and / or 3D data, this information providing a botanical development state. For example, the growth information may include information about size, height, width, a number of plant traits such as leaves or buds, and / or a structure of plant traits such as the aforementioned leaves or buds. This information allows the current state of development of the plant to be identified botanically. Under a derived from the growth information information can be understood, for example, an information or a record in which already a processing of the actually acquired information from the plant (as the growth information) has been done. By way of example, such information derived from the growth information can be understood to mean an image in which a color component has been filtered out if the growth information represents an image of the plant acquired with an optical sensor. The determination of the growth information can be done, for example, automated and without manual intervention. In the present case, storage can be understood to mean storage of the plant parameter data record in a storage unit which is arranged within a device which performs the steps of reading in and of determining. Alternatively or additionally, in the step of storing, a transmission of the plant parameter data record to a central computer, which is arranged outside of the aforementioned device. This transmission can, for example, wired or via radio, d. H. done wirelessly.

The approach presented here is based on the insight that by combining the growth information with the position information of the plant in the plant parameter data set, a (for example botanical) development of the plant can be clearly and reproducibly recorded. In this case, the corresponding growth information can also be assigned to a single plant on the basis of the assigned position information. This advantageously allows a very precise monitoring of the development state of individual plants in a growing area, so that a conclusion on appropriate growth parameters such as applied types and quantities of fertilizer, pesticides, the weather or a special soil condition in the immediate vicinity of each plant in the assessment of the corresponding Growth parameters of the plant can be considered. At the same time, the data collected here can be processed very easily by machine, thereby determining, for example, relationships between growth parameters with correspondingly influenced or changed environmental conditions in the immediate environment of the plant. This makes it possible, for example in seed breeding, to identify the optimal growth parameters for the plant quickly and easily, so that seed can be grown very efficiently with the approach presented here, for example because the selection is based on objective measurement data and thus has a higher information content as subjective credit score.

According to an advantageous embodiment of the approach presented here, the growth information of a single plant can be determined in the step of determining. The determination of the growth information of an individual plant can be effected, for example, by setting a focal length of an optical image if, for example, a camera is used to determine the growth information. It is also conceivable that the outlines or boundaries of the plant are detected by means of an edge detection unit, which determines outlines or boundaries of the plant on an image of the plant in order to identify individual plants. It is also conceivable that a density of green pixels in the image is detected, wherein the individual plant is detected when a significantly higher density of green pixels is determined in a contiguous region of the image compared to another region of the image. In this case, for example, a distinction of a single plant, for example, against a (mostly brown) soil done. Such an embodiment of the approach presented herein offers the advantage of identifying growth parameters of field grown plants with a high spatial resolution on the field. This in turn makes it possible to carry out a very efficient determination of the corresponding growth parameters of the plant.

Also favorable is an embodiment of the approach presented here, in which, in the step of ascertaining as growth information or information derived from the growth information, at least a number and / or size of leaves, a leaf structure, a leaf shape, a position of a blade angle and / or a state of health the plant is determined. Such an embodiment of the approach presented here offers the advantage that the information or parameters mentioned here make possible a very accurate and reliable assessment of, for example, the state of botanical development of the plant. In this way, a very efficient determination of the corresponding growth parameters of the plant is also made possible.

The growth information can be determined in a particularly reliable and reliable manner against disturbing influences if, according to an embodiment of the approach presented here, in the step of ascertaining the growth information or the information derived from the growth information, the plant is viewed from several angles and / or using at least one optical sensor and / or an infrared sensor and / or a depth sensor and / or using an exposure unit is detected, in particular when the exposure unit is designed to shade the plant against exposure to light not emitted by the exposure unit.

The position information of the plant can be determined particularly reliably if, according to a further embodiment of the approach presented here, position information of the plant is read in the reading step, which was determined using a satellite-supported position locating unit, an inertial locating unit and / or an odometric position-detecting unit. or that in the step of storing the plant parameter data record is stored in an electronic storage unit. An odometric position detection unit may, for example, be understood as a position detection unit which, on the basis of sensor signals from sensors on wheels, chains or propellers and / or using at least one imaging system and / or algorithm such as the determination of the optical flow over the bottom of a device, can perform the method proposed herein, recognize their relative movement in the field, and thereby determine their relative position to, for example, a starting point of a motion trajectory across the field.

Particularly advantageous is an embodiment of the approach proposed here, in which the steps of reading, determining and storing are performed at least once repeatedly for the plant, in particular cyclically repeatedly executed, in particular wherein in each step of storing additionally a time parameter is stored, the one Determination time of the growth information contained in the stored plant parameter data set represents. By a time parameter, for example, a date and / or a time can be understood, for example, a growth information is recorded or recorded. Such an embodiment of the approach proposed here offers the advantage of recording the developmental state of the plant in detail, reproducibly and precisely over a relatively long period of time and thus establishing a developmental history for the corresponding plant. This in turn allows this precise determination of the growth parameters for the particular plant and thus, for example, a very efficient breeding of seeds.

In order to be able to make as precise a statement as possible about the state of development or the growth parameters of plants that is statistically easy to assure, the relevant information from a plurality of plants should be stored in the plant parameter data record. According to a particularly favorable embodiment of the approach proposed here, a further position information of another plant can therefore be read in the step of reading, wherein the further position information represents a geographical position of the further plant, and in the step of determining a further growth information of the further plant is determined representing a growth or a development state of the further plant, and in the step of storing the further position information of the further plant and the further growth information of the further plant are stored in the plant parameter data record to capture the plant parameter data record.

Particularly advantageous is an embodiment of the approach presented here, wherein a step of processing the plant parameter data set is provided to obtain from the plant parameter data set a development parameter of at least one plant containing information about a stage of development in the life cycle of the plant or information about a species represents the plant. This makes it very easy to see whether the plant has been damaged, for example, by unexpected local environmental influences such as feeding or dryness, and thus may not be able to provide appropriate growth information for further investigation. It is also conceivable that, instead of the plant in question, a plant of a different genus grows, which can be identified by determining the species of the plant and marked accordingly in the plant parameter data set. For example, in one embodiment, in the step of processing, a classification parameter may further be determined which represents a classification of the plant into a crop class or a weed class, in particular wherein the classification parameter is determined using a distance of the plant from another plant.

Also conceivable is an embodiment of the approach presented here, in which, in the step of storing as a function of the growth information of the plant, a non-consideration information is also assigned and stored in the plant parameter data record. The non-consideration information can be assigned to the plant, for example, if it can be inferred from the growth information or from information derived from the growth information that there is an indication that the plant is not developing or developing as expected. For example, the plant may have been consumed by dryness or drought and thus will not be able to provide any further relevant information or data on its growth or developmental status. Such an embodiment offers the advantage of facilitating the evaluation of the plant parameter data set, since obviously erroneous information in this plant parameter data record no longer has to be taken into account.

Also of advantage is an embodiment of the approach presented here, in which, in the step of processing, the growth information of a plant is also discarded if, in the plant parameter data record, the plant growing at a predetermined geographical position is marked with a disregard information. Non-consideration information should, for example, be understood to mean the presence of information or a marker which indicates that the plant at the corresponding geographical position should no longer be used for the determination of growth parameters. This may, for example, be the case when the plant has entered at the corresponding predetermined geographical position due to dryness or drought and can not provide information relevant to a research target.

It is particularly easy to implement technically in an embodiment of the approach presented here, in which, in the step of determining, storing or processing, the growth information is evaluated, wherein the growth or a state of development of the plant is determined using an in the growth information or one of the growth information derived information contained color information of an optical recording of the plant and / or the position information is evaluated. As a result, information about the state of development or the growth of the plant at the corresponding geographical position can be determined on the basis of camera images of, for example, simple and therefore inexpensive cameras with already widely developed image processing techniques. This allows the cost-effective implementation of the approach proposed here.

Also conceivable is an embodiment of the approach proposed here, in which a wireless transmission of the plant parameter data record to a further processing unit takes place in the step of storing. Such an embodiment of the approach proposed here offers the advantage of being able to transfer the acquired data or the parameter data record directly from the detection location on the field to a powerful evaluation unit so that possible data loss can be prevented as far as possible and one to the geographical positions of the plant moving unit for recording or recording the growth information small and thus can be easily configured. This is particularly interesting if, for example, the method airborne, for example, to be performed in conjunction with a drone.

Also conceivable is an embodiment of the approach presented here in which, in the step of processing, at least one environmental information is taken into account which represents a soil condition, a humidity, an air or soil temperature or another growth parameter influencing the growth of the plant at the geographical position of the plant , In this way, a connection between the environmental information and the information obtained by the plant can be established very efficiently, so that an assessment of the growth parameters of the plant can be very accurate and precise.

This method can be implemented, for example, in software or hardware or in a mixed form of software and hardware, for example in a control unit.

The approach presented here also creates an information system that is designed to implement, control or implement the steps of a variant of a method presented here in corresponding devices. Also by this embodiment of the invention in the form of a device, the object underlying the invention can be solved quickly and efficiently.

For this purpose, the information system can at least one computing unit for processing signals or data, at least one memory unit for storing signals or data, at least one interface to a sensor or an actuator for reading sensor signals from the sensor or for outputting data or control signals to the Actuator and / or at least one communication interface for reading or outputting data embedded in a communication protocol. The computing unit can be, for example, a signal processor, a microcontroller, a PC or the like, wherein the memory unit can be a flash memory, an EPROM or a magnetic memory unit. The communication interface can be designed to read or output data wirelessly and / or by line, wherein a communication interface that can read or output line-bound data, for example, electrically or optically read this data from a corresponding data transmission line or output to a corresponding data transmission line.

In the present case, an information system can be understood to mean an electrical device which processes sensor signals and outputs control and / or data signals in dependence thereon. The device may have an interface, which may be formed in hardware and / or software. In the case of a hardware-based embodiment, the interfaces can be part of a so-called system ASIC, for example, which contains a wide variety of functions of the device. However, it is also possible that the interfaces are their own integrated circuits or at least partially consist of discrete components. In a software training, the interfaces may be software modules that are present, for example, on a microcontroller in addition to other software modules.

In an advantageous embodiment, the information system records or records growth information of a plant in a field. For this purpose, for example, a vehicle or aircraft over a field on which plants are grown, are performed. In this case, the vehicle or aircraft can on the one hand detect geographical position signals, for example of a satellite-supported location system, and combine these with, for example, optically recorded growth information of the plant at the respectively located geographic coordinate in a plant parameter data record. This plant parameter data set can subsequently be evaluated in order to determine the optimal growth conditions at a geographical position of the plant.

Of advantage is also a computer program product or computer program with Program code that may be stored on a machine-readable medium or storage medium, such as a semiconductor memory, a hard disk memory, or an optical memory, and used to perform, implement, and / or control the steps of the method of any of the embodiments described above, especially if the program product or program running on a computer or device.

Embodiments of the invention are illustrated in the drawings and explained in more detail in the following description. It shows:

1 a representation of a use of an embodiment of an information system for detecting at least one plant parameter data set;

2 a schematic representation of an embodiment of an operation of a multi-dimensional biometric measurement of plants in the field; and

3 a flowchart of a method according to an embodiment.

In the following description of favorable embodiments of the present invention, the same or similar reference numerals are used for the elements shown in the various figures and similar acting, wherein a repeated description of these elements is omitted.

1 shows a representation of a use of an embodiment of an information system 100 for detecting at least one plant parameter data record 105 , The information system 100 is doing this in a field 110 applied in which individual plants 115 are grown. Exemplary are in the presentation 1 at the left edge of the field 110 two columns of plants 115 presented, which now with the information system 100 examined or whose data or information is to be recorded and recorded. The information system 100 For this purpose, it can be designed, for example, as an autonomously acting robotic vehicle, a manually operated scanner, a device to be attached to a work machine such as a tractor, or as an aircraft such as a drone, which automatically, for example, according to a predetermined movement trajectory 120 moved and on his / her way the corresponding passed plants 115 examined.

For such an examination of the plants 115 instructs the information system 100 an interface 125 for reading position information 130 one of the plants 115a on. the interface 125 for reading in, for example, can be designed to the geographical position of the plant to be examined 115a using a satellite navigation system 132 such as to detect GPS, whose accuracy can be increased, for example, by using correction signals. Alternatively or additionally, the information system 100 also be formed, the previous movement of the information system 100 according to the movement trajectory 120 , for example with respect to a reference point 135 such as a corner coordinate of the field 110 and thereby determine the geographical position of the plant to be examined 115a to determine. It is also conceivable that the geographical position of the plant to be examined 115a is determined using odometric sensor signals, for example, a number of wheel rotations of wheels 140 of the information system 100 to thereby obtain an exact position of the information system 100 in The Field 110 to obtain. The of the interface 125 read in position information 130 becomes subsequently a memory unit 145 fed.

The information system 100 also includes a unit 150 to determine a growth information 155 the plant to be examined 115a , This unit 150 For example, it can be configured as a simple and therefore cost-effective optical camera which displays an image of the plant to be examined 115a takes up and image information as growth information 155 provides. From this image can then be, for example, a leaf area, a number of leaves, a leaf structure, a pitch angle position, a leaf shape of leaves of the plant to be examined 115a or the like, so that this growth information 155 a conclusion on the growth and / or a state of development of the plant to be examined 115a represents or allows. Additionally or alternatively, the unit may also 150 a depth sensor to the distance of individual components of the plant to be examined 115a to capture and thereby a spatial image of the plant to be examined 115a provide. It is also conceivable that the unit 150 designed to determine the plant to be examined 115a from several directions or multi-dimensional to capture, for example, structures of components of the plant to be examined 115a to resolve with a high accuracy. It is also conceivable that the unit 150 for determining a lighting unit, in order to record the characteristics of the plant to be examined 115a to create the best possible lighting conditions. For this purpose, for example, the lighting unit may be designed to shade the light from other sides than the lighting unit. It is also conceivable that the unit 150 is designed to determine the growth information 155 to process. For example, such processing of the growth information 155 in such a way that an image recorded by the optical camera is filtered in such a way that only a certain color component, for example a green component or a red component, remains in the (now processed) image. This (now processed) image may be, for example, as from the growth information 155 derived information 160 to be viewed as. The one from the unit 150 Growth information provided for determining 155 and / or the growth information 155 derived information 160 can now also the storage unit 145 then the growth information 155 and / or the growth information 155 derived information 160 with the position information 130 linked and in a corresponding plant parameter data set 105 stores. Additionally or alternatively, it is also conceivable that the memory unit 145 is formed, the plant parameter record 105 wired or wireless, as stated in the 1 is shown to an outside of the information system 100 located central computer unit 165 to send. For example, this central computer unit 165 in a farm building outside the field 110 be arranged.

If, for example, a historicization of plant growth in the field 110 grown plants 115 for example, the relationship between different environmental parameters at the geographical positions of the individual plants 115 to recognize their growth or state of development, can with the information system 100 in addition to the study of the previously designated plant 115a also at least one more plant 115b , preferably a plurality or all plants 115 on the field 110 to be examined. This can also be the information system 100 at intervals of, for example, several days, weeks or months, in particular cyclically, the individual in the field 110 grown plants 115 investigate. For this purpose, for example, at each time of investigation, each of the plants concerned 115a respectively. 115b a time parameter, for example in the form of a date and / or time assigned, this information parameter also together with the growth information in question 155 or the information derived therefrom 160 and the position information 130 of the examined plant 115a respectively. 115b in the plant parameter data set 105 is stored.

By the procedure of the information system described above 100 can now be very easily with low cost means a plant data parameter set 105 create, in particular by the preferably automatic acquisition of the growth information 155 or the information derived therefrom 160 a high quality in terms of the reproducibility of the growth information 155 respectively. 160 and also very good for automatically processing the plant parameter data set 105 stored information is suitable.

The manual rating methods described above, on the other hand, are strongly subjectively characterized, because the results of the assessments arise through consideration. Different observers may differ in their assessment in part strongly. Moreover, the division into only nine quality graded levels may be a limitation in the objective assessment of approximately equivalent candidates in the selection of the breed.

The approach presented here is oriented in particular to the automation of parts of the field trials described above. Automated data collection eliminates the disadvantages mentioned and at the same time achieves higher throughput (field trials per time unit and personnel deployment).

Because of the significantly higher data storage capacity of a measuring computer in the information system compared to the recording method described above 100 or the central computer unit 165 further improvement aspects arise. The computerized recording and management of measurement data in the form of the plant parameter data set 105 is down to individual plant level 115a respectively. 115b , In this context, the history of the physiological development of each individual plant measured 115a respectively. 115b be considered in the assessment in the course of breeding, variety selection, seed quality or pesticide use.

The approach presented here, for example, also provides valuable information in the seedling stage as an important development phase of crop plants.

In the young plant stage conclusions can be drawn on the development progress of the individual plant from the actual leaf area as well as from the leaf angle position and other leaf parameters 115a respectively. 115b in relation to the sowing or field emergence date. An advantage of the approach presented here can therefore be seen here in a reproducible designed automatic recording of the leaf parameters of each young plant. This measurement is cyclically repeated during the young plant stage, for example, up to the end of the stock. The detection in the previously known Boniturverfahren allows no Assignment to the single plant as the plant 115a respectively. 115b but an assessment of the development progress of a whole plot in the field 110 , With the extension of viewing and high-throughput measurement in the field 110 considering each individual plant 115a respectively. 115b The approach presented here overcomes the disadvantages of the manual rating method.

Throughout the development phase, there is a history of the development of each plant 115 at their assigned position. The approach presented here goes beyond an automated rating procedure, for example because of the multiple (re-) recognition, counting, measurement and assignment of each individual plant 115a respectively. 115b in stock to a unique position.

• – Bei der Begutachtung der Saatgutqualität im Feldversuchswesen (Saatgut wird in verschiedenen Qualitäten angeboten und für unterschiedliche Standortbedingungen optimiert). Ein Qualitätsmerkmal ist die Feldhomogenität. Eine möglichst gleichmäßige Entwicklung der Aussaat führt zu einem homogenen Bestand. Dies ist für Pflanzenpflege, für die Optimierung von Ertragsparametern und für die Ernte bedeutsam. Homogene Felder können besser (maschinell) bewirtschaftet werden und haben insgesamt einen geringeren Unkrautdruck.
• – Bei der Begutachtung von Züchtungsfortschritten im Feldversuchswesen. Zuchtziele sind z. B. Stresstoleranzen, Resistenzen sowie fruchtartenspezifische Eigenschaften. Ertrag ist ein integrales Zuchtziel, das in Verbindung mit mehreren spezifischen Teilzielen erreicht wird.
• – Bei der Sortenprüfung durch institutionelle Einrichtungen. Die von Saatgutherstellern entwickelten Sorten werden in Bezug auf ihre Merkmale und Eigenschaften durch diese Institutionen geprüft und überwacht. Durch die verbesserte Mess- und Analysetechnik können die Prüfungen schneller, mit höherer Genauigkeit und geringerem Personalaufwand geleistet werden.
• – Bei der Entwicklung und Anwendung von Pflanzenschutzmitteln (PSM). Die Funktion der Pflanzenzählung und Aufzeichnung einer positionsbezogenen Historie kann im Zusammenhang mit dem Einsatz von PSM zu einer Kontrolle und Weiterentwicklung der Leistungsfähigkeit von PSM genutzt werden.
• – Für die kontinuierliche Verbesserung des Wissens über die Eignung der Sorten und Saatgutqualitäten für die weltweit unterschiedlichen Standorte (Einsatzgebiete). Dafür können Ausführungsformen der hier vorgestellten Ansätze auch im täglichen landwirtschaftlichen Betrieb eingesetzt werden. Die beim Anbau gewonnen Daten zur Pflanzenentwicklung sind nach Verknüpfung mit klimatischen Daten und Wetterinformationen in Datenbanken der Saatguthersteller wichtige Indikatoren für die turnusmäßige Eignungsprüfung und Verbesserung der Standortempfehlungen.

High-throughput crop surveying is of great significance for the following objectives:

• - Assessment of seed quality in field trials (seed is offered in different qualities and optimized for different site conditions). A quality feature is the field homogeneity. The most uniform possible development of sowing results in a homogeneous stock. This is important for plant care, for the optimization of yield parameters and for the harvest. Homogeneous fields can be better managed (mechanically) and have a lower weed pressure.
• - When assessing breeding progress in field trials. Breeding goals are z. As stress tolerances, resistance and fruit-specific characteristics. Yield is an integral breeding goal, which is achieved in conjunction with several specific subgoals.
• - At the variety examination by institutional institutions. The varieties developed by seed producers are tested and monitored in terms of their characteristics and characteristics by these institutions. The improved measurement and analysis technology allows testing to be performed faster, with greater accuracy, and less labor.
• - In the development and use of plant protection products (PPPs). The function of plant count and position-related history recording can be used in conjunction with the use of PSM to control and further develop the performance of PSM.
• - For the continuous improvement of knowledge about the suitability of the varieties and seed qualities for the different locations worldwide (fields of application). For this, embodiments of the approaches presented here can also be used in everyday agricultural operations. The data on plant development obtained during cultivation are, after linking with climatic data and weather information in databases of the seed producers, important indicators for the regular suitability testing and improvement of site recommendations.

Due to the progressive climatic changes, this is an important advantage for the seed producers and farmers.

• a.) Eliminierung subjektiver Einflüsse bei Vermessung bzw. Bonitierung von Pflanzen durch Personen
• b.) Zerstörungsfreie mehrdimensionale biometrische Hochdurchsatz-Vermessung der Pflanzen beispielsweise mit folgenden Eigenschaften: – der Einzelblattflächen von Jungpflanzen auf Einzelpflanzenebene – Gesamtblattflächenerfassung einzelner Pflanzen – Erfassung der Blattwinkelstellung – Erfassung der Blattstruktur, Blattform, Blattzahl – Erfassung des (Gesundheits-)Zustands der Einzelpflanze (z. B. Welkegrad, Wassergehalt, Temperatur, Schädlingsbefall, Krankheitsbefall)

The approach presented here in various embodiments solves the disadvantages of the known and extends the applicability by additional advantages:

• a.) Elimination of subjective influences in the measurement or rating of plants by persons
• b.) Non-destructive multi-dimensional biometric high-throughput surveying of the plants, for example with the following characteristics: - the single leaf areas of seedlings on single plant level - total leaf area detection of individual plants - recording of the leaf angle position - acquisition of leaf structure, leaf shape, leaf number - recording the (health) state of the individual plant ( eg Wilt degree, water content, temperature, pest infestation, disease infestation)

• c.) Erkennung jeder einzelnen Pflanze und Zuordnung einer eindeutigen Position (z. B. basierend auf RTK-GPS-Position, Triangulation, Trilateration, Odometrie, etc.)
• d.) Wiedererkennung der einzelnen Pflanze an der bestimmten Position und Dokumentation ihrer physiologischen Entwicklung anhand der Messzyklen ausgehend von Aussaat- und Aufgangstermin
• e.) Erkennen des Verlusts einzelner Pflanzen durch Fraß, Frost, Dürre, etc.
• f.) Hoher Automatisierungsgrad der Messungen durch Einsatz der Messtechnik auf Fahrzeugen/ Fluggeräten mit integrierter autonomer Lokalisierung und Navigation
• g.) Hoher Automatisierungsgrad der Messvorgänge durch rechnergestützte Aufzeichnung und Auswertung großer Datenmengen
• h.) Die mehrdimensionale biometrische Vermessung erfolgt durch kombinierte fotogrammetrische und Entfernungsmessung zum selben Messzeitpunkt für die kurze Dauer einer einzelnen Belichtung. Das schließt den Einfluss von äußeren Faktoren wie z. B. Wind aus.
• i.) Werden nacheinander mehrere (mehrdimensionale)Bildaufnahmen getätigt (beispielsweise während die Messeinrichtung über den Bestand bewegt wird), so erhält man Abbildungen der entsprechenden Pflanzen im Bildausschnitt aus verschiedenen Ausgangspositionen. Die Auswertung der erhaltenen Abbildungen ermöglicht eine Verbesserung der computergestützten Modellbildung (z. B. 3D-Abbild).
• j.) Werden mehrere Sensoren gleichzeitig aus verschiedenen Blickwinkeln auf die zu vermessende Pflanze gerichtet, so kann ebenfalls die Güte der Messergebnisse in Bezug auf Genauigkeit, Verringerung von Abschattungen sowie Verdeckungen erhöht werden.
• k.) Mit Hilfe von Klassifikationsverfahren unterscheidet die Datenmanagementsoftware bzw. die Erfassungseinheit selbst zwischen untersuchter Kulturpflanze und Beikräutern. Diese Klassifikation kann zusätzlich durch a-priori-Wissen (wie z. B. Alinierung, Reihen- und Pflanzabstand der zu untersuchenden Kulturpflanzen) unterstützt werden. Durch die beispielsweise zyklisch erfolgenden Messungen und Klassifikationen ergibt sich eine Verfeinerung der Mess- und Klassifikationsergebnisse, beispielsweise durch Transformation (im Verfahren der Alinierung) des messtechnisch gewonnenen Positionsrasters der Pflanzen.
• l.) Aufgrund der im Datenmanagement beinhalteten Historie jeder Pflanze an ihrer angestammten Position ist eine Korrektur bzw. Verbesserung des Datenbestandes auch rückwirkend möglich (beispielsweise können Fehlzuordnungen nachträglich korrigiert werden).
• m.) Die Datenverarbeitung kann sowohl online (während der laufenden Datenerfassung auf dem Feld) als auch offline (im Nachgang zur Datenaufzeichnung) erfolgen. Die Online-Analyse ermöglicht zudem Plausibilitätsprüfungen der gewonnenen Messdaten, um Fehler und äußere Einflüsse zu minimieren bzw. vor Ort auszuschließen.
• n.) Der Einsatz geeigneter Verfahren zur Datenkompression und -reduktion erlaubt die (längerfristige) Speicherung von Mess- und Analysedaten auf dem Gerät oder eine (Funk-)Übertragung zu einem cloudbasierten Speichersystem.
• o.) Das Datenmanagement gestattet zudem die Sammlung und Analyse von Daten, die durch Geräte oder Verfahren von Drittanbietern aufgenommen wurden. Entsprechende Schnittstellen zum Datenaustausch sind Voraussetzung.
• p.) Die (teil-)automatischen Funktionen tragen zu einer gesamtheitlichen Untersuchung der Pflanzen im Bestand bei (d. h. im Ergebnis erhält der Nutzer ein durchgängiges Gesamtbild der Pflanzen im Bestand während verschiedener Entwicklungsphasen). Dies trifft insbesondere auch bei Kombination mit einer günstigen Hochdurchsatz-Pflanzenzählung zu.
• q.) In einem Ausführungsbeispiel berücksichtigt der hier vorgestellte Ansatz aufgrund einer (teilweisen) Online-Auswertung der gemessenen Daten eine Plausibilitätsprüfung. Damit wird gewährleistet, dass Störungen in der Messeinrichtung (wie z. B. eine verschmutzte Kameralinse) schon zu Beginn der Messung und Aufzeichnung der Daten detektiert werden. Auf diesem Wege wird sichergestellt, dass keine erfolglosen Versuche durchgeführt und Ressourcen geschont werden.

Establishing the relationship of these data with the external factors (such as weather, soil conditions, etc.) is of major importance for the assessment of the breeding, seed treatment or plant protection measures.

• c.) Recognition of each individual plant and assignment of a unique position (eg based on RTK GPS position, triangulation, trilateration, odometry, etc.)
• d.) Recognition of the individual plant at the specific position and documentation of its physiological development based on the measuring cycles based on the sowing and raising dates
• e.) Recognition of the loss of individual plants through feeding, frost, drought, etc.
• f.) High degree of automation of the measurements by using the measurement technology on vehicles / aircraft with integrated autonomous localization and navigation
• g.) High degree of automation of the measurement processes by computer-aided recording and evaluation of large amounts of data
• h.) Multidimensional biometric measurement is performed by combined photogrammetric and ranging measurements at the same time of measurement for the short duration of a single exposure. This excludes the influence of external factors such as B. Wind out.
• i.) If several (multi-dimensional) image recordings are taken one after the other (for example while the measuring device is being moved over the stock), then images of the corresponding plants in the image detail are obtained from different starting positions. The evaluation of the obtained images allows an improvement of the computer-aided modeling (eg 3D image).
• j.) If several sensors are simultaneously aimed at the plant to be measured from different angles, the quality of the measurement results in terms of accuracy, reduction of shadowing and occlusion can also be increased.
• k.) With the aid of classification methods, the data management software or the detection unit itself distinguishes between the examined crop and weeds. This classification can additionally be supported by a priori knowledge (such as, for example, alination, row and plant spacing of the cultivated plants to be investigated). The cyclical measurements and classifications, for example, result in a refinement of the measurement and classification results, for example by transformation (in the method of alination) of the positionally determined position grid of the plants.
• l.) Due to the history of each plant contained in the data management in its original position, a correction or improvement of the database is also possible retrospectively (for example, misallocations can be subsequently corrected).
• m.) The data processing can take place both online (during the current data acquisition on the field) and offline (following to the data recording). The online analysis also enables plausibility checks of the measurement data obtained in order to minimize errors and external influences or exclude them on site.
• n.) The use of suitable methods for data compression and reduction allows (longer-term) storage of measurement and analysis data on the device or a (radio) transmission to a cloud-based storage system.
• o.) Data Management also allows the collection and analysis of data collected by third-party devices or processes. Corresponding interfaces for data exchange are required.
• p.) The (semi) automatic functions contribute to a holistic study of the plants in the herd (ie, the result is that the user obtains a consistent overall picture of the plants in the herd during different stages of development). This is especially true when combined with a favorable high-throughput plant count.
• q.) In one embodiment, the approach presented here takes into account a plausibility check on the basis of a (partial) online evaluation of the measured data. This ensures that faults in the measuring device (such as a soiled camera lens) are already detected at the beginning of the measurement and recording of the data. This ensures that no unsuccessful attempts and resources are spared.

• a. Einen messtechnischen Teil, der die Funktionen Positionszuordnung und mehrdimensionale biometrische Vermessung realisiert.
• b. Einen Datenmanagement-Teil, der die vergleichsweise großen Datenmengen sammelt, archiviert, auswertet und/oder als sinnvolle Reports verarbeitet sowie zur Planung der Messeinsätze dient. Die erfassten Messdaten (in der Form des Pflanzenparameterdatensatzes 105) können je nach Rechenleistung und Datenverbindung direkt auf dem Messgerät bzw. dem Informationssystem 100 verarbeitet oder an einen zentralen Server 165 übertragen werden. Alternativ ist auch die Speicherung auf geeigneten Datenträgern möglich, die im Nachgang zur Messung ausgelesen werden können.

The approach presented here can comprise two components:

• a. A metrological part that realizes the functions position assignment and multidimensional biometric measurement.
• b. A data management part that collects, archives, evaluates and / or processes the comparatively large amounts of data as meaningful reports and also helps to plan the measurement inserts. The acquired measurement data (in the form of the plant parameter data record 105 ) can depending on computing power and data connection directly on the meter or the information system 100 processed or sent to a central server 165 be transmitted. Alternatively, the storage on suitable data carriers is possible, which can be read in the wake of the measurement.

A suitable mobile solution or information system 100 is used for measuring technology or the unit 150 for determining as a carrier system for carrying out field trials. This mobile solution can be designed as a (semi-) autonomous vehicle, manual or motor-driven vehicle, as an attachment for tractors, as an aircraft or in the simplest form in a portable form (for humans or animals). For an optimal construction of the measuring device 150 If necessary, several parallel measuring devices or units can be used 150 be integrated into a structure for determining to be able to make meaningful use of existing lanes or other conditions of the field structure. In this case, the interface should be 125 be adapted to read the position information to the geographical position of each plant 115 to be read by a unit 150 to be investigated. Accordingly, the storage unit should then also be designed to provide the growth information 155 or the information derived therefrom 160 with the respective position information 130 the relevant plant 115 and in the plant parameter data set 105 save.

In order to further parallelize tasks, it is also possible to distribute the measurements over several mobile devices (eg according to the swarm or master-slave principles). The here presented measuring device or the information system 100 has, for example, a position and orientation sensor (eg rtkGPS), and / or optionally supplemented by an orientation sensor (eg an inertial measurement unit IMU), and / or possibly supplemented by odometrically obtained position information.

The information system 100 according to one embodiment, further comprises one or more cameras as (sub-) units 150 for determining with RGB, IR and depth information channels, and a suitable illumination unit for the illumination in the respectively required wavelength range. An optional shading of the image surfaces ensures that the disturbing influence of extraneous light and shadows is minimized. The purpose of the unit provided for shading is designed so that they are the plants 115 not touched during the crossing.

• 1. Gegeben sind die Grenzen und Abmessungen der gesamten Versuchsfläche bzw. des Feldes 110 (Feldgrenzen), die Lage und Ausrichtung der Parzellen und Wege sowie die Trajektorien 120, die sich aus den Saatreihen der Pflanzen 115 ergeben. Diese Daten sind beispielsweise georeferenziert und in einem Vorverarbeitungsschritt erfasst worden (z. B. bei der Anlage der Versuchsflächen oder bei der Aussaat). Diese Geo-Daten werden der Messeinrichtung bzw. Einheit 150 zum Ermitteln bzw. dem Informationssystem 100 bekannt gegeben.
• 2. Die konkrete Versuchsplanung (hier z. B. Aufteilung der Parzellen mit ihrer Zuordnung zu Versuchsobjekten also hier den Pflanzen 115 und Serien) wird ebenso vor dem Beginn der Messungen in die Messeinrichtung eingelesen (Experimentbeschreibung).
• 3. Die Messeinrichtung bzw. das Informationssystem 100 wird reihenweise (z. B. in Drillrichtung) über die Versuchsfläche(n) bzw. das Feld 110 geführt. Dieser Vorgang kann mehrfach stattfinden.
• 4. Während des Screening-Vorgangs (z. B. Überfahrt) werden die in der Messeinrichtung bzw. Einheit 150 zum Ermitteln eingebauten Kameras entsprechend der bekannten Grenzen der Parzellen bzw. des Feldes 110 angesteuert und nehmen Daten auf. Besonders erwähnenswert ist, dass während der Dauer der Überfahrt eine oder mehrere Pflanzen 115 im Bildausschnitt einer (Teil-)Einheit 150 zum Ermitteln erfasst werden. Durch die One-Shot-Bildaufnahmetechnik werden daher beispielsweise mehrere Aufnahmen derselben Pflanze 115a sequenziell gemacht (Image Sequencing). Diese Vorgehensweise gestattet, die Einflüsse durch wechselnde Wind- und Lichtverhältnisse sowie Überlappungen und Rauschen im Bild auf ein Minimum zu reduzieren.
• 5. Die von der Kamera bzw. den Kameras als (Teil-)Einheit(en) 150 zum Ermitteln unter gegebenen Beleuchtungsumständen aufgezeichneten Bilddaten umfassen beispielsweise. a.) RGB-Bilder und b.) Infrarot-Bilder und c.) Tiefenbilder (depth image). Die Kamera(s) sind kalibriert (um z. B. die Korrespondenz zwischen RGB-, IR- und Tiefenbildern zu bestimmen).
• 6. Die von der/den Kamera(s) erfassten Bilder RGB- und IR-Bilder werden beispielsweise miteinander registriert, um z. B. im Anschluss eine Biomasse-Maske (repräsentiert eine Pflanze 115a in 2D) extrahieren zu können.
• 7. Aufgrund der geringen Messgenauigkeit der beispielhaft verwendeten Low-Cost-Kamera wird die beschriebene Vorgehensweise beispielsweise um einen Auswerteschritt ergänzt, der die Fusion aus NDVI-Index (Normalized Differenced Vegetation Index, er wird aus Reflexionswerten im nahen infraroten und sichtbaren roten Wellenlängenbereich des Lichtspektrums gebildet) und dem G(rün)-Anteil des RGB-Bildes umfasst. Im Ergebnis der Fusion entsteht eine hochauflösende Biomasse-Maske in 2D. Damit erfolgt die Trennung von Biomasse und Boden im Bild.
• 8. Um Einzelpflanzen 115a bzw. 115b im Bild zu identifizieren, kommen Positionsinformationen der Einzelpflanzen 115a, 115b, die im Zuge der Feldaufgangszählung bestimmt wurden, oder die bei der Aufnahme der Pflanzenparameter bzw. Wuchsinformation erfasst bzw. eingelesen wurden, zum Einsatz. Vorteil dieser Vorgehensweise ist, dass eine Identifikation der jeweiligen Pflanze 115a bzw. 115b nicht allein anhand der Biomasse-Masken bzw. der Parameter der betreffenden Pflanze 115a bzw. 115b erfolgt, weil auf diese Weise nur als Nutzpflanzen klassifizierte Objekte weiter untersucht werden.
• 9. Die 2D-Biomasse-Maske wird in diesem Verarbeitungsschritt auf das Tiefenbild projiziert. Die zur Biomasse korrespondierenden 3D-Punkte werden ausgeschnitten. Es entsteht eine 3D-Punktewolke der Pflanze, die aufgrund der geringen Messgenauigkeit der Kamera grob verrauscht ist. Zur Rauschunterdrückung kommen Glättungsverfahren zum Einsatz. Im Anschluss erfolgt eine Triangulation der einzelnen Punkte in der Punktewolke mit dem Ziel, die Blattfläche mittels infinitesimal kleiner Teilflächen anzunähern und zu berechnen. Durch Verfeinerung der Auswertemethoden können auf der vorhandenen Datenbasis weitere Pflanzenmerkmale ermittelt werden (z. B. Blattstruktur, Blattstellung, etc.).
• 10. Die Daten bzw. die Wuchsinformation 155 bzw. die daraus abgeleitete Information 160 wird/werden beispielsweise nach Aufzeichnung und Auswertung z. B. in einer Cloud-basierten Umgebung gespeichert und zur Visualisierung und weiteren Analyse auf Endgeräten vorgehalten. Auswertungen können verschiedene tabellarische, grafische und schriftliche Form haben und alle gemessenen und daraus abgeleiteten Parameter enthalten (z. B. maßstäbliche Darstellung der Pflanzen in ihren Parzellen als Draufsicht, Unkrautbesatz im Versuchsfeld, etc.).
• 11. Die in den verschiedenen Versuchsreihen an unterschiedlichen Standorten und über mehrere Jahre gesammelten Daten bilden eine sehr große Menge, die sinnvollerweise automatisiert durch die, z. B. über Cloud-Dienste, gekoppelten dezentralen Messeinrichtungen 160 in einer geeigneten Datenbankstruktur gelagert werden. Dazu sind die Daten auf das notwendige und tatsächliche relevante Maß zu reduzieren oder auch zusätzlich zu komprimieren. Geeignete Algorithmen zur Suche und Filterung gestatten einzelpflanzen-, parzellen-, serien-, versuchs- und standortbezogene sowie weitere sorten- und kulturartübergreifende Auswertungen. Als Beispiel sei hier weiterhin die Nachverfolgbarkeit und Historie einer Einzelpflanze vom Anbau bis zum Konsumenten genannt. Dies bezieht auch Daten ein, die die Datenbank außerhalb der Messeinrichtung gewonnen hat, wie z. B. geobezogene Informationen zum Wetter, Niederschlag, Bodenwerten (Temperatur, Leitfähigkeit, Düngemittelkonzentration, etc.).

In detail, the following exemplary processes and functions in the measurement and evaluation or the information system 100 integrated:

• 1. Given the limits and dimensions of the entire trial area or the field 110 (Field boundaries), the location and orientation of the plots and paths and the trajectories 120 arising from the seed rows of plants 115 result. For example, these data are geo-referenced and recorded in a preprocessing step (eg, when planting the trial plots or sowing). These geo-data become the measuring device or unit 150 for determining or the information system 100 announced.
• 2. The concrete experimental design (here, for example, division of the parcels with their assignment to experimental objects, ie here the plants 115 and series) is also read into the measuring device before the start of the measurements (experiment description).
• 3. The measuring device or the information system 100 is placed in rows (eg in drill direction) over the test area (s) or the field 110 guided. This process can take place several times.
• 4. During the screening process (eg crossing), those in the measuring device or unit 150 for detecting built-in cameras according to the known boundaries of the parcels or the field 110 controlled and take up data. Of particular note is that during the crossing one or more plants 115 in the image section of a (sub-) unit 150 to be detected. For example, the one-shot imaging technique will take multiple shots of the same plant 115a made sequentially (image sequencing). This procedure makes it possible to minimize the effects of changing wind and lighting conditions as well as overlapping and noise in the picture.
• 5. The camera (s) as (sub) unit (s) 150 For example, image data recorded to determine under given lighting conditions includes. a.) RGB images and b.) Infrared images and c.) Depth images. The camera (s) are calibrated (to determine, for example, the correspondence between RGB, IR and depth images).
• 6. The images captured by the camera (s) RGB and IR images are registered with each other, for. B. following a biomass mask (represents a plant 115a in 2D).
• 7. Due to the low accuracy of the example used low-cost camera, the procedure described is supplemented, for example, an evaluation step, the merger of NDVI index (Normalized Differenced Vegetation Index, he is from reflection values in the near infrared and visible red wavelength range of the light spectrum formed) and the G (rün) portion of the RGB image. As a result of the merger, a high-resolution biomass mask is created in 2D. This is the separation of biomass and soil in the picture.
• 8. To single plants 115a respectively. 115b In the picture, positional information of the individual plants come 115a . 115b , which were determined in the course of the field emergence count, or which were recorded or read in when recording the plant parameters or growth information, is used. Advantage of this procedure is that an identification of the respective plant 115a respectively. 115b not solely on the basis of the biomass masks or the parameters of the plant concerned 115a respectively. 115b takes place, because in this way only classified as useful plants objects are further investigated.
• 9. The 2D biomass mask is projected onto the depth image in this processing step. The 3D points corresponding to the biomass are cut out. The result is a 3D point cloud of the plant, which is roughly noisy due to the low accuracy of the camera. Smoothing is used for noise suppression. Subsequently, the individual points in the point cloud are triangulated with the aim of approximating and calculating the leaf area by means of infinitesimally small partial surfaces. By refining the evaluation methods, further plant characteristics can be determined on the existing database (eg leaf structure, leaf position, etc.).
• 10. The data or the growth information 155 or the information derived therefrom 160 becomes / become for example after recording and Evaluation z. B. stored in a cloud-based environment and maintained for visualization and further analysis on devices. Evaluations can have different tabular, graphical and written form and contain all measured and derived parameters (eg scaled representation of the plants in their plots as plan view, weed stock in the trial field, etc.).
• 11. The data collected in the various test series at different sites and over several years constitute a very large amount, which is usefully automated by the, for. B. via cloud services, coupled decentralized measuring equipment 160 be stored in a suitable database structure. For this purpose, the data must be reduced to the necessary and actually relevant level or additionally compressed. Suitable algorithms for searching and filtering allow individual plant, plot, series, experimental and location-related as well as other types of cross-species and cross-cultural analyzes. An example of this is the traceability and history of an individual plant from cultivation to the consumer. This also includes data obtained by the database outside the metering facility, such as: Geo-related information on weather, precipitation, soil values (temperature, conductivity, fertilizer concentration, etc.).

2 shows a schematic representation of an embodiment of an operation of a multi-dimensional biometric measurement of plants in the field. This is done by a carrier platform like the information system 100 first over the interface 125 for reading a position information 130 read. This position information 130 for example, from a sensor 200 in or at the interface 125 can be read in, for example, read or capture the GPS data and thereby a geographical position of the information system 100 can determine. Furthermore, the carrier platform or the information system comprises 100 at least one unit 150 for determining at least one growth information 155 a plant 115 , The unit 150 For example, to determine one or more sensor system (s) 205 which are provided with an optical detection device, such as a low-cost optical camera, which images of the plants 115 recorded and this as growth information 155 or derived information 160 the storage unit 145 transmitted. In the storage unit 145 For example, then a data extraction and / or a data fusion of the growth information 155 and / or the information derived therefrom with the position information 130 respectively. It can, for example, as in the field 210 of the 2 is shown in more detail, an assignment 215 the geographical position of the plant 115 be made. For this purpose, for example, from a memory 220 a geographic location known from plant count (for example, using GPS data) of the plant 115 used and / or used for plausibility. Furthermore, a concrete acquisition 225 the plant 115 in the form of growth information 155 or the information derived from it 160 so that, for example, this information is in the plant parameter record 105 be filed. Here, in the plant parameter data set 105 also information about extracted plant characteristics, such as a number of leaves, a leaf structure or the like of the respective plant 115 be filed.

Additionally, it is conceivable that in the storage unit 145 or one of the 2 not shown corresponding control unit is detected that, for example, an exposure of the camera 205 area is too weak, so that an exposure unit (for example, as a subunit of the unit 150 to determine) to the brightness in the camera 205 range, in particular increase. Such a drive signal 230 for example, from the storage unit 145 or in the 2 not shown control unit to the unit 150 to be issued for determination.

The plant parameter record 105 can then, for example, to a processing unit 235 which are either part of the information system 100 or, for example, in a central computer 165 is arranged, as exemplified in the 2 is shown. In this processing unit 235 For example, processing is then performed in a 4D processing level, as exemplified in the field 240 of the 2 is shown. In this 4D processing level, the plant parameter data set is evaluated 105 , Especially the preferably three-dimensional recorded features of the plant 115 with the associated times as the fourth dimension, so that from this information a very precise inference to the growth or development of the respective plant 115 can be pulled. Here are the geographical positions 245 that are present as GPS data, for example, with data 250 concerning a history of the development of the individual plant 115 or the re-identification of the individual plant 115 , For example, based on the growth information, and the recognized plant traits 255 the relevant plant 115 linked to this, for example, the temporal growth of the individual plant 115 be able to determine at the respectively assigned geographical position. This allows, for example, knowing the nature of the soil, the water content of the soil, weather and / or climate data at the geographical position of the field, assessing the relationship of environmental influences on the growth of the plant to recognize the geographical position and thereby, for example, a seed breeding optimize. It is also conceivable that due to processing results in the processing unit 235 executed algorithms an annotation 238 takes place, in which a change in the data of the memory unit 145 stored plant parameter data set 105 he follows. Here, for example, a correction can be made such that upon detection of a dead plant 115a respectively. 115b For example, by animal feeding or by drought the plant information for the plant in question 115a respectively. 115b additional information is obtained that this plant 115a respectively. 115b then for a further assessment of the plants grown in the field 115 is no longer to be considered.

Furthermore, the plant parameter data set 105 in a database 260 , for example, directly in the central computer 165 , a PC or a cloud, not shown here. Also conceivable is a change in the plant parameter data set 105 that from the database 260 by means of a change signal 265 is triggered.

The plant parameter record 105 or results of a statistical evaluation of information from the plant parameter data set 105 may also be in a presentation unit 265 visually displayed. From this results can be 270 determine or improve seed quality, a particularly favorable method of growing seed, development of varieties of the plant, development of the use of plant protection products, etc. Also conceivable here is a change in the plant parameter data set 105 that of the presentation unit 265 by means of a change signal 275 is triggered.

3 shows a flowchart of an embodiment of the approach presented here as a method 300 for capturing at least one plant parameter data set of a plant growing on a field. The procedure 300 includes a step 310 reading position information of the plant, the position information representing a geographical position of the plant. Furthermore, the method comprises 300 one step 320 determining growth information of the plant that represents a growth or a state of development of the plant. Finally, the process includes 300 one step 330 storing the plant position information and the plant growth information or information derived from the growth information as a plant parameter data set to acquire the plant parameter data set.

If an exemplary embodiment comprises a "and / or" link between a first feature and a second feature, then this is to be read so that the embodiment according to one embodiment, both the first feature and the second feature and according to another embodiment either only first feature or only the second feature.

Claims (15)

Procedure ( 300 ) for capturing at least one plant parameter data set ( 105 ) one on a field ( 110 ) growing plant ( 115a . 115b ), the process ( 300 ) comprises the following steps: - reading in ( 310 ) a position information ( 130 ) of the plant ( 115a . 115b ), the position information ( 130 ) a geographical position of the plant ( 115a . 115b represents; - Determine ( 320 ) growth information ( 155 ) of the plant ( 115a . 115b ), which is a growth or a state of development of the plant ( 115a . 115b represents; and - save ( 330 ) the position information ( 130 ) of the plant ( 115a . 115b ) and the growth information ( 155 ) of the plant ( 115a . 115b ) or one of the growth information ( 155 ) derived information ( 160 ) as plant parameter data set ( 105 ), the plant parameter data set ( 105 ) capture. Procedure ( 300 ) according to claim 1, characterized in that in the step of determining ( 320 ) the growth information ( 155 ) of a single plant ( 115a . 115b ) is determined. Procedure ( 300 ) according to one of the preceding claims, characterized in that in the step of determining ( 320 ) as growth information ( 155 ) or the growth information ( 155 ) derived information ( 160 ) at least one total leaf area, a number of leaves, a single leaf area, a leaf structure, a leaf shape, a position of a blade angle and / or a health status of the plant ( 115a . 115b ) is determined. Procedure ( 300 ) according to one of the preceding claims, characterized in that in the step of determining ( 320 ) of the growth information ( 155 ) or the growth information ( 155 ) derived information the plant ( 115a . 115b ) from a plurality of viewing angles and / or using at least one optical sensor ( 205 ) and / or an infrared sensor and / or a depth sensor and / or using an exposure unit, in particular wherein the exposure unit is designed to control the plant ( 115a . 115b ) to shade from exposure by light not emitted by the exposure unit. Procedure ( 300 ) according to one of the preceding claims, characterized in that in the step of reading ( 310 ) a position information ( 130 ) of the plant ( 115a . 115b ) read using a satellite-based position locating unit ( 132 ), an inertial location unit and / or an odometric position detection unit, and / or that in the step of storing ( 330 ) the plant parameter data set ( 105 ) in an electronic storage unit ( 145 ) is stored. Procedure ( 300 ) according to one of the preceding claims, characterized in that the reading steps ( 310 ), Determining ( 320 ) and save ( 330 ) for the plant ( 115a . 115b ) are executed at least once, in particular cyclically repeatedly executed, in particular wherein in each step of the storing ( 330 ) additionally a time parameter is stored, the one determination time of the stored in the plant parameter data record ( 105 ) growth information ( 155 ). Procedure ( 300 ) according to one of the preceding claims, characterized in that in the step of reading ( 310 ) another position information ( 130 ) of another plant ( 115a . 115b ), the further position information ( 130 ) a geographical position of the further plant ( 115a . 115b ) and in the step of determining ( 320 ) a further growth information ( 155 ) of the further plant ( 115a . 115b ), which determines a growth or a state of development of the further plant ( 115a . 115b ) and in the step of storing ( 330 ) the further position information ( 130 ) of the further plant ( 115a . 115b ) and the further growth information ( 155 ) of the further plant ( 115a . 115b ) in the plant parameter data set ( 105 ) are saved to the plant parameter data set ( 105 ) capture. Procedure ( 300 ) according to one of the preceding claims, characterized in that in the step of storing ( 330 ) depending on the growth information ( 155 ) of the plant ( 115a . 115b ) is also assigned a non-consideration information and in the plant parameter data set ( 105 ) is stored. Procedure ( 300 ) according to one of the preceding claims, characterized by a step of processing the plant parameter data set ( 105 ) to extract from the plant parameter data set ( 105 ) a development parameter of at least one plant ( 115a . 115b ), which provides information about a stage of development in the life cycle of the plant ( 115a . 115b ) or information about a species of the plant ( 115a . 115b ). Procedure ( 300 ) according to claim 8, characterized in that in the step of processing further the growth information ( 155 ) of a plant ( 115a . 115b ) or the information derived therefrom ( 160 ) is discarded if in the plant parameter data set ( 105 ) the plant growing at a predetermined geographical position ( 115a . 115b ) is marked with a disregard information. Procedure ( 300 ) according to one of the preceding claims, characterized in that in the step of determining ( 320 ), Save ( 330 ) or processing an evaluation of the growth information ( 155 ), whereby the growth or a state of development of the plant ( 115a . 115b ) using a in the growth information ( 155 ) or one of the growth information ( 155 ) derived color information of an image of the plant ( 115a . 115b ) and / or position information ( 130 ) is evaluated. Procedure ( 300 ) according to one of the preceding claims, characterized in that in the step of storing ( 330 ) a wireless transmission of the plant parameter data set ( 105 ) to another processing unit ( 165 ) he follows. Information system set up to perform steps of the procedure ( 300 ) according to one of the preceding claims in corresponding units ( 125 . 150 . 154 ) execute and / or implement. Computer program adapted to perform the procedure ( 300 ) according to one of the preceding claims. A machine readable storage medium storing the computer program of claim 13.

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