WO2022049580A1

WO2022049580A1 - Methods for artificial pollination and apparatus for doing the same

Info

Publication number: WO2022049580A1
Application number: PCT/IL2021/051077
Authority: WO
Inventors: Thai (ELGRABLI) SADE; Avi Keren; Ido-Ad SENESH
Original assignee: Bumblebee A.I Ltd.
Priority date: 2020-09-02
Filing date: 2021-09-02
Publication date: 2022-03-10
Also published as: EP4208011A1; AU2021336124A1; IL301099A; AU2021336124A9; EP4208011A4; MX2023002520A; PE20230909A1; CA3193650A1; CL2023000588A1

Abstract

Universal, in-field, well-controlled, scalable, easy-to-use, and cost-effective methods and systems for artificial pollination and yield increase are provided.

Description

METHODS FOR ARTIFICIAL POLLINATION AND APPARATUS FOR DOING THE SAME

TECHNICAL FIELD OF THE INVENTION

The invention relates to the field of artificial pollination. More specifically, the invention relates to novel systems and methods for pollination management, and to methods of increasing the yield of a crop.

BACKGROUND OF THE INVENTION

Pollination is a process that involves transferring pollen grains from the male part of a plant to the female part of a plant (stigma) . This process is done naturally by wind, small birds, insects (especially bees) , and more. This process is crucial for later fertilization, production of seeds and the formation of fruits and vegetables. Disappearance of different natural pollinators, a phenomenon that if exacerbated, will severely harm the production of different crops that rely almost exclusively on insect pollination Moreover, the globalization process has shifted the production of different crops from their natural habitat into different geographies where the natural pollinators that evolved alongside the crop are not present. Their absence has a crucial effect on the quality of pollination, as the local pollinators may not be able to efficiently pollinate due to the inefficient pollen extraction, unsuitable body geometry and size, lack of attraction to the crop' s nectar and pollen, which, altogether directly affect the crop's yield and quality. In addition, the insect-based pollination process depends on insect behavior, which could be tampered by weather conditions, temperatures, and other non-human controlled conditions . Artificial pollination is a solution helping to overcome the difficulties mentioned above in a controlled and efficient way, thus providing an increase in crop yields and quality. Currently available solutions suffer from various deficiencies. For example, hand pollination is the simplest and cheapest artificial pollination method. Using different tools such as brushes one may gently collect pollen grains from a male flower and apply it directly to the female flower's stigma. Another method of hand pollination is rubbing the male organs of cut flowers on the female organs of the pollinated flowers. These methods are useful for a small scale, however, require a skilled human labor for applying on a large scale . Other solutions offer massive harvesting and processing of flowers to extract pollen grains. The collected pollen powder is then applied on the flowers with different techniques. This complicated process is both expensive and time-consuming, and it could not be performed in the field or orchard itself. This method is suitable for a certain type of crops with short-term mass flowering and a large number of pollen grains per flower.

Providing scalable, in-f ield/in-orchard, cost-effective, well-controlled and easy-to-use systems and methods for artificial pollination, yield increase, and f iled/orchard management remains a long and unmet need.

SUMMARY OF THE INVENTION

Accordingly, it is a principal object of the present invention to provide a universal, in-field, well-controlled, scalable, easy-to-use, and cost-effective methods and systems for artificial pollination and yield increase.

The invention provides a method of artificial pollination of a flowering plant in need of pollination comprising: a . providing a pollination apparatus , wherein said apparatus comprises a vibration element ; an airflow generating element ; and, optional ly, an el ectrostatic force generating element ; b . generating vibration at the flowering plant by the vibration element ; c . activating the airf low-generating element to create a cloud of pollen grains in the vicinity of the flowering plant ; and, optionally, d . applying electrostatic forces to the pol len grains of the flowering plant by the electrostatic force generating element ; wherein said flowering plant is buzz-pollinated plant .

The invention further provides a pol lination apparatus , comprising : a vibration element configured to generate vibration at the target plant ; an airflow generating element configured to generate a cloud of pollen at the vicinity of the target plant ; and, optionally, an electrostatic force generating element conf igured to apply electrostatic forces to the pollen grains of the target plant ; wherein the target plant is a flowering plant .

The invention further provides an arti ficial pollination system for the pollination of at least one flowering plant , comprising an operator, and the pollination apparatus comprising : a vibration element configured to generate vibration at the target plant ; an airflow generating element configured to generate a cloud of pollen at the vicinity of the target plant ; and, optionally, an electrostatic force generating element conf igured to apply electrostatic forces to the pollen grains of the target plant ; wherein the target plant is a flowering plant, wherein said plant is buz z-pollinated plant . The invention further provides a method of increasing a number of fruits per plant, comprising pollinating said plant at the flowering stage using an artificial pollination system of the invention.

The invention further provides a method of increasing a number of seeds per fruit on a plant, comprising pollinating said plant at the flowering stage using an artificial pollination system of the invention.

The invention further provides a method of artificial pollination of multiple flowering plants comprising: a. providing the artificial pollination system according to the embodiments of the invention; b. collecting data by the data acquisition unit; optionally, c. selecting the location of the pollination based on the data acquired by the data acquisition unit; optionally, d. selecting the timing of the pollination based on the data acquired by the data acquisition unit; and, e. pollinating the multiple flowering plants.

The invention further provides a computer implemented method of artificial pollination of an area comprising flowering plants in need of pollination comprising: a. providing the artificial pollination system according to the embodiments of the invention; b. collecting data by the data acquisition unit; c. processing the image data and assessing the state of the flowering plant based on a set of parameters indicative of the state of the plant extracted from the image data; d. providing instructions to the controller based on the state of the flowering plant; optionally, e. selecting the location of the pollination based on the state of the flowering plant; optionally, f. selecting the timing of the pollination based on the data acquired by the data acquisition unit; and, g. pollinating the multiple flowering plants.

Additional features and advantages of the invention will become apparent from the following drawings and description.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig.lA-D are a block diagrams of exemplary embodiments of the pollination apparatus of the invention;

Fig.2 is a flowchart of an exemplary embodiment of the system of the invention;

Fig.3 is a flowchart of an exemplary embodiment of the method of artificial pollination of a flowering plant;

Fig.4 is a flowchart of an exemplary embodiment of the method of artificial pollination of multiple flowering plants ;

Fig.5 is a flowchart of an exemplary embodiment of a computer implemented method of artificial pollination of an area comprising flowering plants;

Fig.6 illustrates results of the field trial showing an increase in a yield of the blueberry crop; and,

Fig.7 illustrates results of the field trial showing an statistically increase in a yield of the blueberry crop.

DETAILED DESCRIPTION OF THE INVENTION

Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is applicable to other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting.

According to some embodiments, the invention provides method of artificial pollination of a flowering plant in need of pollination. Reference is now made to Figure 3, illustrating an exemplary embodiment of the above method. Method of artificial pollination of a flowering plant comprises: providing a pollination apparatus, wherein said apparatus comprises a vibration element; an airflow generating element; and, optionally, an electrostatic force generating element [1000] ; generating vibration at the flowering plant by the vibration element [2000] ; activating the airflow-generating element to create a cloud of pollen grains in the vicinity of the flowering plant [3000] ; and, optionally, applying electrostatic forces to the pollen grains of the flowering plant by the electrostatic force generating element [4000] .

According to some embodiments of the above method, the pollination apparatus comprises a vibration element.

According to some embodiments, the pollination apparatus comprises vibration element and an airflow generating element .

According to some embodiments, the pollination apparatus comprises a vibration element, and at least one of an airflow generating element and electrostatic force generating element.

According to some embodiments, the pollination apparatus comprises a vibration element, an airflow generating element and electrostatic force generating element. According to some embodiments, the flowering plant is buzz- pollinated plant. As used herein, the term "buzz-pollinated plant" refers, without limitation, to a plant having a unique anther shape compared to other flora. In buzz pollinated plants, this process can only happen if pollinators visit the flowers to extract pollen. The flower morphology of buzz pollinated plants is different from other flora that do not use this type of pollination. The anthers are completely sealed except for a small pore at the top or have very small slits that open along the sides. The pores and slits are small enough that insects cannot easily enter the anther, but large enough pollen can exit. Because of this shape, they are often referred to as poricidal anthers. These poricidal anthers are only able to release pollen when vibrated at a specific frequency. The stigmas of these flowers are often located below the anthers. In one embodiment, the flowering buzz-pollinated plant is a berry plant. In the context of the invention the term "berry" is meant to be understood in its common meaning and not necessarily in the botanical sense. The nonlimiting list of berry plants of the invention includes blueberry, cranberry, berberis, bearberry, bilberry, blackcurrant, huckleberry, redcurrant, tomato, eggplant, and capsicum; or any other berry plant that may benefit from the method and/or apparatus and/or system according to the embodiments of the invention.

According to some embodiments of the above method, the nonlimiting list of additional buzz-polinated plants includes: Arbutus unedo, Billardiera scandens Euclea crispa, boquila, Ugni molinae, Gaultheria hispida , date-plum, Gaultheria procumbens, Lonicera caerulea, Triphasia tri folia, Vaccinium vitis-idaea, Aristotelia chilensi s , Vaccinium floribundum, Mahonia aqui folium, Billardiera long! flora , Vaccinium parvi folium, Gaultheria shallon, Rubus spectabilis , Tamarillo , Diospyros texana, or any other plant that may benefit from the method of the invention .

According to some embodiments of the above method, the vibration element and the airflow-generating element act simultaneously. Typically, the vibration element is configured to generate vibration at the bottom part of the flowering plant and to cause release of the pollen. In one embodiment vibration is generated at the central part of the plant. In one embodiment, vibration precedes air-flow generation. In another embodiment, the vibration and airflow are generated simultaneously. In yet another embodiment, the airflow generation precedes vibration. As used herein, the term "vibration" refers to a periodic motion of the particles of an elastic body or medium in alternately opposite directions from the position of equilibrium when that equilibrium has been disturbed. In one embodiment, the vibration is generated mechanically. In another embodiment, the vibration is generated using, without limitation, air pressure or any other applicable technique. As used herein the term "airflow" refers to a situation when air behaves in a wave like manner, meaning particles naturally flow from areas of higher pressure to those where the pressure is lower. Specifically, the airflow generates turbulent airflow for creating a cloud of pollen grains and reattach to the stigmas pollen grains which intended to hit the ground.

According to some embodiments, the invention provides a pollination apparatus, comprising: a vibration element configured to generate vibration at the target plant; optionally, an airflow generating element configured to generate a cloud of pollen at the vicinity of the target plant; and, optionally, an electrostatic force generating element configured to apply electrostatic forces to the pollen grains of the target plant . Reference i s now made to Figure 1A i llustrating an exemplary embodiment of the poll ination apparatus of the invention . The apparatus 10 , comprises a main body 100 encompassing a control unit 400 , a power source 600 , and optionally a data col lecting unit 500 . The main body 100 is operably engaged with a plant contact unit 200 via a plant contact mechanism 300 . The plant contact unit 200 is configured to become engaged with the flowering plant 700 and to facilitate the poll ination act . The pollination act is facilitated through one or more modules which can become operably engaged with the plant contact unit 200 . Reference is now made to Figure 1B-D illustrating exemplary embodiments of the plant contact unit 200 . In one embodiment, illustrated on Figure IB, the plant contact unit comprises a vibration element 800 , conf igured to generate vibration of the f lowering plant 700 and to thereby release the pollen grains . In another embodiment , illustrated on Figure 1C , the plant contact unit comprises a vibration element 800 and an airflow generating element 900 . The vibration element 800 triggers pollen release while the airflow generating element 900 generates a cloud of pollen grains in the vicinity o f the flowering plant 700 . The vibration element 800 and the airf low generating element 900 can work simultaneously and/or concurrently . In one embodiment illustrated on Figure ID, the plant contact unit comprises a vibration element 800 , an airflow generating element 900 , and electrostatic force generating element 950 . The pollination apparatus of the invention can have dif ferent conf igurations (not shown) and may encompass various units and/or modules and/or features that may contribute to the performance of the apparatus . According to some embodiments, the apparatus of the invention further comprises at least one of: an image acquisition element, a data collecting unit, and an internal control unit.

According to some embodiments, the invention provides an artificial pollination system for the pollination of at least one flowering plant, comprising an operator, and the pollination apparatus of the invention, wherein said plant is characterized by having bell-shaped flowers. In one embodiment, the operator is a human operator. In another embodiment, the operator is an automated operator. According to some embodiments, the system further comprises a server and a data-acquisition module in-communication with the server, wherein said data acquisition module is configured to collect data and to transmit said data to the server. In one embodiment, the server is in-communication with the operator and configured to instruct the operator to pollinate the at least one plant. In another embodiment, the server is configured to instruct the operator to seize pollinating the at least one plant. In one embodiment, the data acquired by the data-acquisition unit are selected from environmental data, plant-related data, or a combination thereof. According to some embodiments, the data acquisition module comprises a plurality of sensors. As used herein, the term "sensor" refers, without limitation to a device that detects and responds to some type of input from the physical environment. The specific input could be light, heat, motion, moisture, pressure, or any other environmental input. The non-limiting list of sensors of the invention includes one or more IR cameras, temperature sensor, humidity sensor, LIDAR, Sound recorder, GNSD, 4D imaging sensor, hyperspectral imaging, IMU, light sensor, or any other applicable sensor, or a combination thereof. In one embodiment, the data acquisition module comprises a plurality of sensors of the same type. In another embodiment, the data acquisitions module comprises a variety of different sensors. In one embodiment, the data acquisition module comprises on-demand set of sensors. In one embodiment, the combination of different sensors is defined by the user. In one embodiment, the data acquisition module comprises a preset combination of sensors .

According to some embodiments, the environmental data comprise data indicative of the plant status. In one embodiment, the environmental data comprise data indicative of the pollination efficiency. As used herein, the term "environmental data" refers, without limitation, to any information and/or input related to the state of the environment and the impacts on ecosystems. The environmental data maybe acquired by the sensors of the data acquisition module, and/or can be gathered from external sources, such as, without limitation, weather forecast, activity of insects, or any other information that might be of relevance. The data acquired by the sensors may include, without limitation, heal th/disease state, yield estimations, flowering stage, and others.

In one embodiment, the system of the invention is further configured to process real-time data and to pollinate the agricultural area based on said real-time data. As used herein, the term "real-time" refers, without limitation to relating to a setting in which input data are processed within milliseconds so that it is available virtually immediately as feedback. In one embodiment, the real-time data are selected from the data acquired by the plurality of sensors, data collected from an external source, or a combination thereof. According to some embodiments the system of the invention is configured to pollinate multiple plants . In one embodiment, the system is configured to selectively pollinate multiple plants .

Reference i s now made to Figure 2 illustrating a schematic diagram of an exemplary embodiment of a pollination system according to the embodiments of the inventi on 500 comprising a pollination apparatus 10 , an operator 20 , data acquisition module 30 , a server 40 , a controller 50 , and other components , such as , without limitation, a power source (not shown) , an internal controller (not shown ) , data storage element (not shown) , navigation system (not shown) , user interface (not shown) , this in order to optimi ze and improve the entire pollination process . The sensors ' function is to collect environmental data ( such as , without limitation, temperature , humidity, visible/non-visible light , and/or sound) about the plant status . The navigation system with GNSS , mems and other navigation sensors can provide data on the current location and the inertial position of the worker on the tree . The user interface might include a screen or instruction lights for guiding the operator where to do the poll ination . A wireless connection may be used to send and receive data from the remote server/ servers .

According to some embodiments , the invention provides a method of increasing a yield of a crop, comprising poll inating said a crop at the flowering stage using an arti ficial pollination system according to the embodiments of the invention . In the context of the invention, the term "crop" is fully interchangeable with the terms "plant" and " flowering plant" .

According to some embodiments , the invention provides a method of artif icial pol lination o f multiple flowering plants. Reference is now made to Figure 4 illustrating an exemplary embodiment of the above-method comprising: providing the artificial pollination system according to the embodiments of the invention [5000] ; collecting data by the data acquisition unit [6000] ; optionally, selecting the location of the pollination based on the data acquired by the data acquisition unit [7000] ; optionally, selecting the timing of the pollination based on the data acquired by the data acquisition unit [8000] ; and, pollinating the multiple flowering plants [9000] .

According to some embodiments, the invention provides a computer implemented method of artificial pollination of an area comprising flowering plants in need of pollination. Reference is now made to Figure 5, illustrating an exemplary embodiment of the above method comprising: providing the artificial pollination system according to the embodiments of the invention [7000] ; collecting data by the data acquisition unit [8000] ; processing the image data and assessing the state of the flowering plant based on a set of parameters indicative of the state of the plant extracted from the image data [9000] ; providing instructions to the controller based on the state of the flowering plant [10000] ; optionally, selecting the location of the pollination based on the state of the flowering plant [11000] ; optionally, selecting the timing of the pollination based on the data acquired by the data acquisition unit [12000] ; and, pollinating the multiple flowering plants [13000] .

In one embodiment, said assessing the state of the flowering plant comprises using a trained neural network. In another embodiment, said step of processing comprises steps of computing said image data using computer implemented algorithm trained to generate output based on the image data . In yet further embodiment , the said computer implemented algorithm is trained to generate output based on predetermined feature vectors or attributes extracted from the image data . In one embodiment, said method comprises steps of implementing with said algorithm a training process according to a training dataset comprising a plural ity of training images of a plurali ty of flowering plants captured by the at least one imaging sensor, wherein each respective training image of the plurali ty o f training images is associated wi th the state of said flowering plant depicted in the respect ive training image .

According to some embodiments , said training process comprises steps of : capturing images o f the flowering plant using an imaging sensor ; classi fying images into desired categories by applying a tag associated with parameters or attributes indicative of the state of the flowering plant extracted from the image data ; and applying a computer vision algorithm to determine a set of feature vectors associated with each desired category .

According to some embodiments , said training process comprises steps of : capturing images of the flowering plant using an imaging sensor ; classi fying images into des ired categories by tagging certain obj ects in the images and labeling said obj ects with desired classes ; and , applying a computer vision algorithm to determine a set o f feature vectors associated with each desired category .

According to some embodiments , the method comprises steps of applying a machine learning process with the computer implemented trained algorithm to determine the state of the imaged flowering plant . In one embodiment , said algorithm is implemented with a machine learning process using a neural network with the processed data . In yet further embodiment , said machine learning process comprises computing by the at least one neural network, a tag of at least one desired category for the at least one flowering plant , wherein the tag of at least one class i fication category is computed at least according to weights of the at least one neural network, wherein the at least one neural network i s trained according to a training dataset comprising a plurality of training images of a plurality of flowering plants captured by the at least one imaging sensor, wherein each respective training image of the plurality of training images is associated with said tag of at least one desired category of at least one flowering plant depicted in the respective training image ; and generating according to the tag of at least one clas si fication category, instructions for execution by the controller . Alternatively, machine learning process comprises computing by the at least one neural network, a tag of at least one desired class for the at least one type of plant , wherein the tag of at least one class is computed at least according to weights of the at least one neural network, wherein the at least one neural network is trained according to a training dataset comprising a plural ity of training images of a plurality of plants captured by the at least one imaging sensor, wherein each respective training image of the plurality o f training images is associated with said tag of at least one desired class of at least one plant type depicted in the respective training image ; and generating according to the tag of at least one clas s , instructions for execution by the controller .

According to some embodiments , the arti ficial pollination system according to the embodiments of the invention is conf igured to increase a yield of a crop . As used herein, the term "yield" or " agricultural productivity" or " agricultural output" refers , without l imitation, to the measure of the yield of a crop per unit area of land cultivation, and/or the seed generation of the plant itself. In the context of the invention, the increase in the yield can be measured according to any suitable parameter, such as, without limitation, average fruit weight, average fruit size, number of seeds per fruit, and using any applicable methodology known in the art and/or in-use by growers.

According to some embodiments of the artificial pollination system of the invention and methods according to the embodiments of the invention are configured to increase a yield of a crop by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 110%, 115%, 120%, 125%, 130%, 135%, 140%, 145%, 150%, 155%, 160%, 170%, 175%, 180%, 185%, 190%, 195% and 200% .

According to some embodiments, additional purpose of the invention is to disclose an holistic method for in- f ield/in-orchard pollination process: collection of pollen is performed using the electrostatic pollen collecting device; pollen application is performed using different methods such as electrostatic spraying, air pumps or any other application method; data collected using the data acquisition module which is used to collect environmental data that will later be sent to a remote server using wireless connection. Those in the art will understand that a server may be a single computer, a network of computers, either in the cloud or on a local machine; the server will process the data and will provide insights to the grower; the server will provide recommendations about necessary future actions for the grower to take, using the device "user interface" (for real time actions) and using the dashboards (for real-time and/or offline actions) . Those in the art will understand that serving the data could be supplied on di f ferent platforms , such as website , mobile appl ication, tablet application and other platforms avai lable in the market . The insights and actions that the server may provide include , without limitation, pollination ef ficiency in the orchard; recommendation on areas to re poll inate arti ficial ly due to inef ficient pollination ; reporting on pests and physical damages in orchard/ field; water condition; temperature and humidity near the crops .

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention .

According to some embodiments , the method of the present invention comprises steps o f applying a machine learning process with the computer implemented trained algorithm to determine the state of the plant . Thus , it is within the scope of the present invention that the algorithm ( or computer readable program) is implemented with a machine learning process us ing a neural network with the processed data . The term " training" in the context of machine learning implemented within the system of the present invention refers to the process of creating a machine learning algorithm. Training involves the use of a deep learning framework and training dataset . A source of training data can be used to train machine learning models for a variety of use cases , from failure detection to consumer intell igence . The neural network may compute a clas si fication category, and/or the embedding, and/or perform clustering, and/or detect obj ects from trained clas ses for identi fying state of an individual plant in the context of pollination . As used herein the term "class" refers , without limitation, to a set or category of things having some property or attribute in common and differentiated from others by kind, type, or quality.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the funct ions/acts .

As used herein, the term "classifying" may sometimes be interchanged with the term clustering or tagging, for example, when multiple plant images are analyzed, each image may be classified according to its predefined feature vectors and used to creating clusters, and/or the plant images may be embedded and the embeddings may be clustered. The term "desired category" may sometimes be interchanged with the term embedding, for example, the output of the trained neural network in response to an image of a plant may be one or more classification categories, or a vector storing a computed embedding. It is noted that the classification category and the embedding may be outputted by the same trained neural network, for example, the classification category is outputted by the last layer of the neural network, and the embedding is outputted by a hidden embedding layer of the neural network.

The architecture of the neural network (s) may be implemented, for example, as convolutional, pooling, nonlinearity, locally connected, fully connected layers, and/or combinations of the aforementioned.

It is noted that the tagging and classifying of the plants in the images or the plant state characteristic targets may be manually or semi manually entered by a user (e.g., via the GUI, for example, selected from a list of available phenotypic characteristic targets) , obtained as predefined values stored in a data storage device, and/or automatically computed.

The term "feature vector" refers hereinafter in the context of machine learning to an individual measurable property or characteristic or parameter or attribute of a phenomenon being observed e.g., detected by a sensor. It is herein apparent that choosing an informative, discriminating, and independent feature is a crucial step for effective algorithms in pattern recognition, machine learning, classification and regression. Algorithms using classification from a feature vector include nearest neighbor classification, neural networks, and statistical techniques. In computer vision and image processing, a feature is an information which is relevant for solving the computational task related to a certain application. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. When features are defined in terms of local neighborhood operations applied to an image, a procedure commonly referred to as feature extraction is executed .

Example 1: Artificial pollination in blueberry plants

Field 'trial design:

Varieties :

• Main variety: 5

• Flowering time: Oct - Feb

• Secondary variety: 6

Flowering time: Oct Feb Tunnel :

• In each tunnel there are 4 rows composing 70 plants. In each tunnel 1 row is of a secondary variety (to allow cross pollination) .

• Tunnel area: 0.05 hectare

• Row length: 35m

• Number of plants in row: 70

Planting structure:

• Distance between rows: 1.7m

• Planting: Detached bedding

Pollinators :

• BB (Bumblebees) - 2 hives / dunham

• HB (Honey-bees) - No

Agronomical Variation taking into account as basic trail conditions. Assumptions

• No pest control spraying will be applied during the flowering season.

• All tunnels will be subjected equally to the following treatments: The tunnels chosen for the trials are with the same characteristics:

• Pruning method

• Fertilizers

• Plant' s age

• Planting direction

Applied treatments

Evaluation 1 - Percentage of yield increase on a commercial block of 0.15 hectares Method :

Demonstrate the operation of pollination-cart over the course of an entire flowering season on a commercial 0.15 hectare block.

Treatments / field actions:

• Treatment 1 - Pollination with test device (Self pollination) periodically every 7 days.

• Control - untreated tunnel.

Field map:

The treatment rows on each variety were selected randomly, as represented in Table 1 :

Fruit harvest measurements:

For an entire harvesting season:

• fruit weight per row

• Average weight of harvested fruit from two randomly picked rows.

(All units are per hectare)

• Total yield of the entire experiment including control.

• Average fruit weight measured on a percentage of total yield .

Analysis • Means and variances of groups were be calculated.

• A Z-Test will be performed to determine the difference between the groups. An ANOVA test may also be appropriate . Results

In this trial randomly selected tunnels (4) were pollinated using test technology, untreated randomly selected tunnels (4) served as the control group. All tunnels were also pollinated by honey bees. Two tunnels for each treatment were picked and one (out of two) harvest was weighted. The results are represented in Table 2, Table 3, and Figure 6.

Table 2: Weight measurements for the second harvest

Table 3: Yield response

Example 2 : Increase of Blueberries crops yield by using artificial pollination device of the invention.

Treatments / field actions:

Treatment 2 - Tunnel was divided into two sections using a physical barrier that divided each row into two half rows. Pollination with test technology (Self pollination) was performed in one section of the tunnel, while the rest of the tunnel was used as control and was untreated and included natural bee activity. Treatment was applied periodically every 5 days.

Field configuration is represented in Table 4 :

Harvest measurements:

During the entire harvesting season: • Perform fruit count on each section compared with control .

• Perform fruit weigh measurements compared with control on a percentage basis.

Results • Total yield expressed as per hectare for each section.

• Average fruit size per section.

Harvest measurements: During the entire harvesting season :

• Perform fruit count of treated row compared with control ( control for this row wi ll be the average row yield of all the variety 5 ) . • Perform fruit weigh measurements of treated row compared with control on a percentage basis

Results :

As represented in table 5 and on Fig . 7 , use of the test technology lead to statistically signi f icant increase in average weight per row, as well as about 30% increase in yield response .

Table 5 : Average yield per row in treatment versus control .

The results of the experiments that were conducted under di f ferent geographical conditions showed that the arti ficial pollination system of the invention has superior performance when compared to control . The superiority of the system of the invention was demonstrated by an increased yield response . Moreover, contribution of the individual components of the system of the invention to the performance were as sessed . The experimental results showed that vibration has s igni ficant impact on yield increase . The combination of vibration with airflow generation has also positive ef fect on the yield while compared to the control . As used herein, the singular forms "a, " "an" and "the" are intended to include plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" or "comprising, " when used in this specification, specify the presence of stated features, integers, steps, operations, elements components and/or groups or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups or combinations thereof. As used herein the terms "comprises", "comprising", "includes", "including", "having" and their conjugates mean "including but not limited to". The term "consisting of" means "including and limited to".

As used herein, the term "and/or" includes any and all possible combinations or one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative ("or") .

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and claims and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Well-known functions or constructions may not be described in detail for brevity and/or clarity.

It will be understood that when an element is referred to as being "on, " "attached" to, "operatively coupled" to, "operatively linked" to, "operatively engaged" with, "connected" to, "coupled" with, "contacting," etc., another element, it can be directly on, attached to, connected to, operatively coupled to, operatively engaged with, coupled with and/or contacting the other element or intervening elements can also be present. In contrast, when an element is referred to as being "directly contacting" another element, there are no intervening elements present. Whenever the term "about" is used, it is meant to refer to a measurable value such as an amount, a temporal duration, and the like, and is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, or ±0.1% from the specified value, as such variations are appropriate to perform the disclosed methods .

It will be understood that, terms such as, for example, "processing", "computing", "calculating", "determining", "establishing", "analyzing", "checking", or the like, may refer to operation (s) and/or process (es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulates and/or transforms data represented as physical (e.g., electronic) quantities within the computer's registers and/or memories into other data similarly represented as physical quantities within the computer's registers and/or memories or other information non- transitory storage medium that may store instructions to perform operations and/or processes.

It will be understood that, although the terms first, second, etc., may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. Rather, these terms are only used to distinguish one element, component, region, layer and/or section, from another element, component, region, layer and/or section. Certain features of the invention, which are , for clarity, described in the context o f separate embodiments , may also be provided in combination in a single embodiment . Conversely, various features of the invention, which are , for brevity, described in the context of a single embodiment , may also be provided separately or in any suitable sub-combination or as suitable in any other described embodiment of the invention . Certain features described in the context of various embodiments are not to be considered essential features of those embodiments unless the embodiment is inoperative without those elements .

Throughout this application, various embodiments o f this invention may be presented in a range format . It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inf lexible limitation on the scope of the invention . Accordingly, the description o f a range should be cons idered to have speci fically di sclosed all the possible subranges as well as individual numerical values within that range . For example , description of a range such as from 1 to 6 should be considered to have speci fically disclosed subranges such as from 1 to 3 , from 1 to 4 , from 1 to 5 , from 2 to 4 , from 2 to 6 , from 3 to 6 etc . , as well as individual numbers within that range , for example, 1 , 2 , 3 , 4 , 5 , and 6 . This applies regardless of the breadth of the range .

Whenever a numerical range is indicated herein, it is meant to include any cited numeral ( fractional or integral ) within the indicated range . The phrases "ranging/ranges between" a first indicate number and a second indicate number and "ranging/ranges from" a first indicate number "to" a second indicate number are used herein 1 interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.

Whenever terms "plurality" and "a plurality" are used it is meant to include, for example, "multiple" or "two or more". The terms "plurality" or "a plurality" may be used throughout the specification to describe two or more components, devices, elements, units, parameters, or the like. The term set when used herein may include one or more items. Unless explicitly stated, the method embodiments described herein are not constrained to a particular order or sequence. Additionally, some of the described method embodiments or elements thereof can occur or be performed simultaneously, at the same point in time, or concurrently.

All publications, patent applications, patents, and other references mentioned. The disclosures of these publications in their entireties are hereby incorporated by reference into this application to more fully describe the state of the art to which this invention pertains. In case of conflict, the patent specification, including definitions, will prevail. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting. Throughout this application various publications, published patent applications and published patents are referenced.

It will be appreciated by persons skilled in the art that the present invention is not limited to what has been particularly shown and described hereinabove. Rather the scope of the present invention is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description .

Claims

1. A method of artificial pollination of a flowering plant in need of pollination comprising: a) providing a pollination apparatus, wherein said apparatus comprises a vibration element; an airflow generating element; and, optionally, an electrostatic force generating element; b) generating vibration at the flowering plant by the vibration element; c) activating the airflow-generating element to create a cloud of pollen grains in the vicinity of the flowering plant; and, optionally, d) applying electrostatic forces to the pollen grains of the flowering plant by the electrostatic force generating element; wherein said flowering plant is buzz-pollinated plant.

2. The method of claim 1, wherein the flowering plant is a berry plant.

3. The method of claim 2, wherein the berry plant is selected from a blueberry plant, a cranberry plant, a berberis plant, a bilberry plant, a blackcurrant plant, and a huckleberry plant .

4. The method of any one of claim 1 to 3, wherein the vibration element and the airflow-generating element act simultaneously .

5. The method of any one of claim 1 to 3, wherein vibration precedes airflow-generation.

6. A pollination apparatus, comprising: a vibration element configured to generate vibration at the target plant; an airflow generating element configured to generate a cloud of pollen at the vicinity of the target plant; and, optionally, an electrostatic force generating element configured to apply electrostatic forces to the pollen grains of the target plant ; wherein the target plant is a flowering plant . The pollination apparatus of claim 6 , wherein the vibration element and the airflow generating element are configured to work simultaneously . The pollination apparatus of claim 6 , wherein the vibration element and the airflow generating element are configured to work subsequently to each other . The pol lination apparatus of any one of claims 6 to 8 , further compris ing at least one of : an image acqui sition element, a data collecting unit , and an internal control unit . An arti ficial pollination system for the pollination of at least one flowering plant, comprising an operator, and the apparatus of claim 6 or 7 , wherein said plant i s characteri zed by having bell-shaped flowers . The system o f claim 10 , wherein the operator is a human operator . The system o f claim 10 , wherein the operator is an automated operator . The system of any one of claims 10 to 12 , further compris ing a server and a data-acquisition module in-communication with the server, wherein said data acqui sition module is conf igured to collect data and to transmit said data to the server . The system of claim 13 , wherein said server is incommunication with the operator and configured to instruct the operator to pollinate the at least one plant . The system o f claim 13 or 14 , wherein the server is conf igured to instruct the operator to sei ze pollinating the at least one plant . The system of any one of claims 13 to 15, wherein the data acquired by the data-acquisition unit are selected from environmental data, plant-related data, or a combination thereof . The system of any one of claims 13 to 16 further configured to process real-time data and to pollinate the at least one plant based on said real-time data. The system of claim 17, wherein the real-time data are selected from the data acquired by the plurality of sensors, data collected from an external source, or a combination thereof . The system of any one of claims 10 to 18 configured to pollinate multiple plants. The system of claim 19 configured to selectively pollinate multiple plants. A method of increasing a number of fruits per plant, comprising pollinating said plant at the flowering stage using an artificial pollination system of any one of claims 10 to 20. A method of increasing a yield of a crop, comprising pollinating said a crop at the flowering stage using an artificial pollination system of any one of claims 10 to 20. A method of artificial pollination of multiple flowering plants comprising: a) providing the artificial pollination system of any one of claims 10 to 20; b) collecting data by the data acquisition unit; optionally, c) selecting the location of the pollination based on the data acquired by the data acquisition unit; optionally, d) selecting the timing of the pollination based on the data acquired by the data acquisition unit; and, e) pollinating the multiple flowering plants. The method of any one of claims 21 to 23, wherein the flowering plant is a buzz-pollinated plant. The method of any one of claims 21 to 24, wherein the flowering plant is a berry plant. The method of any one of claims 21 to 25, wherein the flowering plant is characterized by bell-shaped flowers. A computer implemented method of artificial pollination of an area comprising flowering plants in need of pollination comprising : a) providing the artificial pollination system of any one of claims 10 to 20; b) collecting data by the data acquisition unit; c) processing the image data and assessing the state of the flowering plant based on a set of parameters indicative of the state of the plant extracted from the image data; d) providing instructions to the controller based on the state of the flowering plant; optionally, e) selecting the location of the pollination based on the state of the flowering plant; optionally, f) selecting the timing of the pollination based on the data acquired by the data acquisition unit; and, g) pollinating the multiple flowering plants. The method of claim 27, wherein said assessing the state of the flowering plant comprises using a trained neural network . The method of claim 27, wherein said step of processing comprises steps of computing said image data using computer implemented algorithm trained to generate output based on the image data. The method of claim 29, wherein said computer implemented algorithm is trained to generate output based on predetermined feature vectors or attributes extracted from the image data. The method of claim 19 or 30, wherein said method comprises steps of implementing with said algorithm a training process according to a training dataset comprising a plurality of training images of a plurali ty of f lowering plants captured by the at l east one imagi ng sensor, wherein each respective training image of the plurali ty of training images i s associated with the state of said flowering plant depicted in the respective training image . The method of claim 31 , wherein said training process comprises steps of a ) capturing images of the flowering plant using an imaging sensor ; b ) classi fying images into desired categories by applying a tag associated with parameters or attributes indicative of the state of the flowering plant extracted from the image data ; and c ) applying a computer vision algorithm to determine a set of feature vectors associated with each desired category . The method of claim 32 , comprises steps o f applying a machine learning process with the computer implemented trained algorithm to determine the state of the imaged flowering plant . The method of claim 33 , wherein said algorithm is implemented with a machine learning process using a neural network with the processed data . The method of claim 34 , wherein said machine learning process comprises computing by the at least one neural network, a tag o f at least one desired category for the at least one flowering plant , wherein the tag of at least one clas si fication category is computed at least according to weights of the at least one neural network, wherein the at least one neural network is trained according to a training dataset comprising a plurality of training images of a plurality of flowering plants captured by the at least one imaging sensor, wherein each respective training image of the plurality of training images is associated with said tag of at least one desired category of at least one flowering plant depicted in the respective training image ; and generating according to the tag o f at least one clas si fication category, instructions for execution by the controller .