CN115841621A - Tea tree disease identification system based on unmanned aerial vehicle remote sensing data - Google Patents
Tea tree disease identification system based on unmanned aerial vehicle remote sensing data Download PDFInfo
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Abstract
The invention discloses a tea tree disease identification system based on unmanned aerial vehicle remote sensing data, wherein the disease and insect condition of tea trees directly influences the yield and quality of tea leaves, and the traditional tea tree disease monitoring has the problems of time and labor consumption, low efficiency and high cost. Therefore, the invention combines the unmanned remote sensing technology with the control of tea plant diseases, utilizes the unmanned aerial vehicle to collect the hyperspectral image data of the tea plant canopy, corrects the collected image data, calculates the chlorophyll content and the leaf area index of the tea plant canopy according to the reflectance data measured by hyperspectral remote sensing, and effectively pre-judges the disease and insect pest conditions of the tea garden in advance by comparing and analyzing the spectral characteristics of healthy and disease insect-state tea leaves. Therefore, the high-frequency regular monitoring requirement of the disease and pest information of the tea trees can be met, and the general investigation efficiency of the disease and pest of the tea trees is improved.
Description
Technical Field
The invention relates to the field of tea tree maintenance, in particular to a tea tree disease identification system based on unmanned aerial vehicle remote sensing data.
Background
During the growth and development process of the tea trees, the tea trees face the damage of diseases and insect pests at any time, and the diseases and insect pests can cause poor quality of the tea leaves and yield reduction, thereby causing serious economic loss. At present, common tea garden pest control methods in China include pesticide spraying, insect net trapping and the like, the efficiency is low, the control effect is limited, and the used chemical pesticide can cause certain harm to human bodies and tea garden environments. The monitoring and early warning system can monitor tea trees timely and effectively and provide early warning, is favorable for controlling the development of diseases and insect pests of the tea trees, reduces economic loss to the maximum extent and improves economic benefits. At present, the traditional manual general investigation method is mainly adopted to find the plant diseases and insect pests in the tea trees, the labor intensity is high, the financial resources are consumed, and the general investigation efficiency is low. And if tea growers utilize unmanned aerial vehicle remote sensing technology, can carry out accurate detection to the tea garden environment, understand the growth situation of tea tree, combine advanced technologies such as big data, artificial intelligence, carry out the analysis to the degree of suffering from a disaster, the reason of suffering from a disaster of tea tree to formulate scientific and effective prevention and cure scheme, save the pesticide volume of spraying, improve the comprehensive efficiency in tea garden.
The hyperspectral remote sensing technology is one of the international means for monitoring the spectral characteristic change of crop pests at present, and has the characteristics of high spectral resolution, integrated maps, more wave bands, strong continuity, large spectral information amount and the like. Use unmanned aerial vehicle as the carrier, carry on sensors such as hyperspectral imager and acquire earth's surface remote sensing image, handle the image information of gathering through the computer, this has combined that unmanned aerial vehicle is light, easy operation and hyperspectral imager can gather advantages such as ground thing continuous spectrum information, can monitor and the prevention and cure of diseases and insect pests to large tracts of land tea mountain convenient and fast ground. The application of hyperspectral data makes information extraction more advantageous. The method is a research hotspot and key for the hyperspectral remote sensing to be used for monitoring the crop diseases and insect pests at present. In view of the above problems, a solution is proposed as follows.
Disclosure of Invention
The invention aims to provide a tea tree disease identification system based on unmanned aerial vehicle remote sensing data, which has the advantages that an overall and local pest and disease damage checking scheme can be obtained more comprehensively, and the technical problems of waste of manpower resources and incapability of fine pesticide application operation at present are solved.
The technical purpose of the invention is realized by the following technical scheme:
a tea tree disease identification system based on remote sensing data of an unmanned aerial vehicle comprises a data monitoring and receiving system, an unmanned aerial vehicle autonomous navigation system and a data transmission and cloud computing platform,
the data monitoring and receiving system comprises:
the monitoring and positioning module is used for positioning the unmanned aerial vehicle set to acquire the flight coordinate and slope elevation data;
the system comprises a sensor remote sensing module, a data acquisition module and a data acquisition module, wherein the sensor remote sensing module is used for controlling and receiving monitoring data acquired by an unmanned aerial vehicle, the monitoring data comprises visible spectrum data, the visible spectrum data is high-precision spectrum image data generated by a tea tree leaf surface disease spectrum image, the high-precision spectrum image data comprises a plurality of wave band spectrum image data, the sensor module is used for providing the visible spectrum data, and the sensor module is used for providing flight coordinates and slope altitude data;
the artificial algorithm module is used for correcting and processing the precision and the contrast of the image, and the artificial algorithm is used for performing edge calculation of correction processing on high-precision spectral image data;
the data receiving and storing module is based on an STM32F103 and is used for transmitting and storing the monitoring data from the peripheral to a local memory through a synchronous DMA short pulse transmission protocol to generate local memory data;
the data transmission and cloud computing platform comprises:
the data transmission unit DTU is wireless terminal equipment which converts the local memory data into IP data and transmits the IP data through a 4G mobile information system;
the tea quilt disease degree index calculation module is preset with disease degree grades, obtains the tea quilt disease degree values according to the transmitted high-precision visible spectrum data and the spectrum image data of a plurality of wave bands, and obtains first plan data for providing prevention and treatment suggestions;
the geographical position section integration module is used for integrating a tea garden pest and disease distribution vector diagram from the flight coordinate and the slope elevation and obtaining second plan data;
the insect-repelling disease calculation module is used for inputting the first preset data and the second preset data into a neural network spraying model to obtain insect disease checking and printing data;
the pest and disease damage checking and hitting model is used for forming a pest and disease damage checking and hitting scheme according to pest and disease damage checking and hitting data, and the pest and disease damage checking and hitting scheme comprises a whole tea garden checking and hitting scheme and a local checking and hitting key scheme;
the unmanned aerial vehicle autonomous navigation system comprises a flight control system, an unmanned aerial vehicle positioning system and a vision processing system.
The sensor module acquires surface remote sensing image data and corrects the image data, wherein the correction includes radiation correction of the acquired hyperspectral image data, denoising, and geometric correction.
Preferably, the manual algorithm is used for adjusting the output of a filter for the local variance of the hyperspectral image data, establishing a model of each wave band of the image data, and realizing the radiation correction of the hyperspectral image data by using the model; denoising the hyperspectral image data after the radiation consistency correction by using a wiener filtering algorithm; and repairing the defect value of the hyperspectral image data by adopting a double channel.
Preferably, the unmanned aerial vehicle positioning system comprises a directional target with known reflectivity change, the directional target is erected in a tea garden, and the sensor module acquires hyperspectral image data of the calibrated target.
Preferably, the neural network spraying model is used for inputting the grades of the pesticide to be sprayed one by one for calculation and controlling the spraying amount of the pesticide by using the output vector of the neural network spraying model.
The pest and disease damage checking and printing model is used for forming a pest and disease damage checking and printing scheme according to the pest and disease damage checking and printing data;
the pest and disease damage checking and hitting scheme comprises a tea garden (tea slope) overall checking and hitting scheme and a local checking and hitting scheme;
the monitoring data monitored by the sensor remote sensing module and the flight coordinate and slope altitude data provided by the positioning system are acquired synchronously by the double ADC of the STM32 singlechip, and are stored in the sensor remote sensing module through a DMA protocol. As the visible spectrum data capable of reflecting the growth information of the tea crops are only at the special wave band positions in all the visible spectrum wave bands, the sensor module can firstly screen out the visible spectrum data, and the data and the positioning data are transmitted to the DTU through a modbus protocol, so that the use process is more automatic.
The above is the edge processing process of the system data monitoring and receiving, and the specific data transmission and cloud processing processes are detailed below:
through DTU with data conversion IP data and through the wireless terminal equipment that 4G mobile information system carried out the transmission, the high in the clouds is rectified and is denoised the content of gathering to carry out preferred and concatenation to the partial data after rectifying, save usable result, and constitute complete plant diseases and insect pests regional vector diagram. And screening a corresponding disease and pest checking scheme from the library by the cloud computing platform through the vector diagram.
Unmanned aerial vehicle automatic navigation's automatic cruise also can leave in high in the clouds and unmanned aerial vehicle organism after setting for. For the variable files which can be modified at any time in the automatic cruising middle route and the cruising time, a plurality of different sub-files and alternative files can be provided, and the cruising operation process is recorded at least twice in the same sight so as to obtain high-spectrum data with higher accuracy.
The utility model provides a tea tree disease identification system based on unmanned aerial vehicle remote sensing data, including unmanned aerial vehicle and high in the clouds platform, unmanned aerial vehicle's carrying on includes:
an STM32 embedded main control module based on an ARM Cortex-M3 processor;
the visible spectrum sensor is used for collecting hyperspectral image data of the tea garden;
the storage module is used for storing the acquired hyperspectral image and the positioning information;
the 4G communication module transmits the collected hyperspectral image and positioning information to the cloud platform and positions the hyperspectral image and the positioning information through WIFI
The invention has the beneficial effects that:
(1) Through the WIFI positioning method, the three-dimensional reconstruction can be performed on the tea garden designed by the equal-height ladder layers, the monitoring requirements of plant diseases and insect pests at different heights and frequency slopes can be met, and the fine general investigation efficiency of the tea garden is improved.
(2) Regular cruise monitoring of a tea garden effectively frees up productivity.
(3) Compared with the method for extracting the relevant growth information of the tea trees with single parts, the method can comprehensively and purposefully realize the investigation and killing of plant diseases and insect pests based on the structural vector diagram of the tea garden slope.
(4) And a cloud edge cooperative mode is adopted, so that the processing capacity of the whole system is accelerated, and the utilization degree is improved.
Drawings
FIG. 1 is a schematic block diagram of a tea tree disease identification system based on unmanned aerial vehicle remote sensing data according to an embodiment;
FIG. 2 is a schematic diagram of data transmission of the tea tree disease recognition system based on unmanned aerial vehicle remote sensing data according to the embodiment.
Detailed Description
The following description is only a preferred embodiment of the present invention, and the protection scope is not limited to the embodiment, and any technical solution that falls under the idea of the present invention should fall within the protection scope of the present invention.
The invention provides a tea tree disease identification system based on unmanned aerial vehicle remote sensing data, which adopts a subsystem processing mode and comprises an unmanned aerial vehicle and a cloud platform, wherein the unmanned aerial vehicle is provided with the following carrying components: an STM32 embedded main control module based on an ARM Cortex-M3 processor; the visible spectrum sensor is used for collecting hyperspectral image data of the tea garden; the storage module is used for storing the acquired hyperspectral image and the positioning information; the 4G communication module transmits the collected hyperspectral image and positioning information to the cloud platform and positions the hyperspectral image and the positioning information through WIFI
The unmanned aerial vehicle system is used for general survey of Tetranychus californicus in Guijiling, and the method comprises the following steps:
(1) Firstly, paving four gradient and four known targets on a slope under a Himalayan mountain foot according to the ground; the positioning of the target is obtained by signal transmission; the four gradients are designed to guide the flight displacement of the unmanned aerial vehicle at different slope heights;
(2) Acquiring visible hyperspectral image data of the tea trees by using a visible spectrum sensor; in the shooting process, shooting at least twice in the same place, and judging the resolvable resolution of the obtained data by calculating the overlapping rate;
(3) Correcting hyperspectral image data obtained by shooting and storing the hyperspectral image data by a DMA (direct memory access) protocol; changes in weather elements (e.g., rain) can cause different colored lighting values to be captured for different presentations; carrying out correction processing by using a manual algorithm, taking an overlapping value of a plurality of acquired pictures in the same place, removing part of wave bands of different wave points, and carrying out consistency correction; the corrected data is stored with the positioning data through a DMA protocol;
(4) The stored data are transmitted to the DTU through a modbus protocol, and the DTU transmits the acquired data to the cloud platform through 4G mobile communication for analysis and realization;
(5) The cloud platform acquires the plant diseases and insect pests searching and killing scheme through further correction and integration, and the STM32 main controller also controls the flight of the unmanned aerial vehicle simultaneously to this realizes more accurate, efficient tea garden plant diseases and insect pests information general survey.
The above embodiments are described in further detail to solve the technical problems, technical solutions and advantages of the present invention, and it should be understood that the above embodiments are only examples of the present invention and are not intended to limit the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A tea tree disease identification system based on unmanned aerial vehicle remote sensing data is characterized by comprising a data monitoring and receiving system, an unmanned aerial vehicle autonomous navigation system and a data transmission and cloud computing platform,
the data monitoring and receiving system comprises:
the monitoring and positioning module is used for positioning the unmanned aerial vehicle set to acquire the flight coordinate and slope elevation data;
the system comprises a sensor remote sensing module, a data acquisition module and a data acquisition module, wherein the sensor remote sensing module is used for controlling and receiving monitoring data acquired by an unmanned aerial vehicle, the monitoring data comprises visible spectrum data, the visible spectrum data is high-precision spectrum image data generated by a tea tree leaf surface disease spectrum image, the high-precision spectrum image data comprises a plurality of wave band spectrum image data, the sensor module is used for providing the visible spectrum data, and the sensor module is used for providing flight coordinates and slope altitude data;
the artificial algorithm module is used for correcting the processing precision and the contrast for the image, and the artificial algorithm is used for performing edge calculation of correction processing on high-precision spectral image data;
the data receiving and storing module is based on an STM32F103 and is used for transmitting and storing the monitoring data from the peripheral to a local memory through a synchronous DMA short pulse transmission protocol to generate local memory data;
the data transmission and cloud computing platform comprises:
the data transmission unit DTU is wireless terminal equipment which converts the local memory data into IP data and transmits the IP data through a 4G mobile information system;
the tea quilt disease degree index calculation module is preset with disease degree grades, obtains the tea quilt disease degree values according to the transmitted high-precision visible spectrum data and the spectrum image data of a plurality of wave bands, and obtains first plan data for providing prevention and treatment suggestions;
the geographical position section integration module is used for integrating a tea garden pest and disease distribution vector diagram from the flight coordinate and the slope elevation and obtaining second plan data;
the insect-repelling disease calculation module is used for inputting the first preset data and the second preset data into a neural network spraying model to obtain insect disease checking and printing data;
the pest and disease damage checking and hitting model is used for forming a pest and disease damage checking and hitting scheme according to pest and disease damage checking and hitting data, and the pest and disease damage checking and hitting scheme comprises a whole tea garden checking and hitting scheme and a local checking and hitting key scheme;
the unmanned aerial vehicle autonomous navigation system comprises a flight control system, an unmanned aerial vehicle positioning system and a vision processing system.
2. The tea tree disease identification system based on unmanned aerial vehicle remote sensing data as claimed in claim 1, wherein the sensor module obtains surface remote sensing image data and performs correction processing on the image data, and the correction processing comprises performing radiation correction on the collected hyperspectral image data, performing denoising processing, and performing geometric correction.
3. The tea tree disease identification system based on unmanned aerial vehicle remote sensing data is characterized in that the manual algorithm is used for adjusting the output of a filter for the local variance of the hyperspectral image data, establishing each wave band model of the image data, and realizing the radiation correction of the hyperspectral image data by utilizing the model; denoising the hyperspectral image data after the radiation consistency correction by using a wiener filtering algorithm; and repairing the defect value of the hyperspectral image data by adopting a double channel.
4. The tea tree disease identification system based on unmanned aerial vehicle remote sensing data as claimed in claim 1, wherein the unmanned aerial vehicle positioning system comprises a directional target with known reflectivity change, the directional target is erected in a tea garden, and the sensor module collects hyperspectral image data of the calibrated target.
5. The tea tree disease recognition system based on unmanned aerial vehicle remote sensing data as claimed in claim 1, wherein the neural network spraying model is used for inputting the levels of the amount of pesticide to be sprayed one by one for calculation and controlling the spraying amount of pesticide as the output vector.
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