Accurate measurement of the downwash flow field of plant protection unmanned aerial vehicles (UAVs) is essential for analyzing the spatial distribution of droplets. To realize on-line rapid detection of the downwash flow field of a multi-rotor UAV, a flexible polypropylene detection device based on the principle of full bridge strain effect was proposed. Its performance principle was based on the physical deformation caused by wind pressure. The Fluid Flow and Static Structural modules of ANSYS 16.0 finite element software were used to simulate one-way fluid-solid coupling interaction. The surface of the resistive strain gauge embedded in the flexible detecting structure responded well to wind speed variation, hence it was suitable for downwash airflow wind field detection. By solving the strain force on the surface of the flexible detection structure, the length and layout of the grating wire of the strain gauge on the surface of the flexible detection structure were optimized. Meanwhile at 4 m·s-1 wind speed, the output voltage at varied bridge flexible acquisition systems in the acquisition card was measured. Results indicated coefficient of variation of 3.67%, 1.63% and 1.5%, respectively, which proved the good data acquisition consistency of the system. Through calibration test, the regression equation for the relationship between output voltage and wind speed for three unique sensor signal measuring circuits was established. The determination coefficients R2 for single bridge, half bridge and full bridge circuits were 0.9885, 0.9866 and 0.9959, respectively. In conclusion, by applying the multi-rotor plant protection UAV test platform, the results indicated the maximum relative error of the wind speed at each sampling point of the system at 1.0 m altitude was below 5.61%. Simulated and measured value had an RMSE maximum error of 0.1246 m·s-1. Moreover, downwash airflow detection not only has high accuracy but also has high sensitivity. Thus, there is convenience and practicability in the plant protection offered by this approach. The rapid measurement of UAV wind field and the established two-dimensional wind field model can provide a basis for precise application of agricultural aviation.

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Design of UAV Downwash Airflow Field Detection System Based on Strain Effect Principle

Uniform seed distribution within the row is the prime objective of precision planters for better crop growth and yield. Inclined plate planters are generally used for sowing bold seeds like maize, groundnut, chickpea, and their operating parameters like the forward speed of operation, the seed metering plate inclination, and the seed level in the hopper affect the cell fill and subsequently the uniform seed distribution. Therefore, to achieve precise seed distribution, these parameters need to be optimized. In the present study, out of the different optimization techniques, a new intelligent optimization technique based on the integrated ANN-PSO approach has been used to achieve the set goal. A 3–5-1 artificial neural network (ANN) model was developed for predicting the cell fill of inclined plate seed metering device, and the particle swarm optimization (PSO) algorithm was applied to obtain the optimum values of the operating parameters corresponding to 100% cell fill. The most appropriate optimal values of the forward speed of operation, the seed metering plate inclination, and the seed level in the hopper for achieving 100% cell fill were found to be 3 km/h, 50-degree, and 75% of total height, respectively. The proposed integrated ANN-PSO approach was capable of predicting the optimal values of operating parameters with a maximum deviation of 2% compared to the experimental results, thus confirmed the reliability of the proposed optimization technique.

Optimizing the seed-cell filling performance of an inclined plate seed metering device using integrated ANN-PSO approach

Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

The invention discloses an on-orbit self-propelled type harvesting device for a greenhouse leaf vegetable harvesting robot and a harvesting method. According to the technical scheme adopted by the invention, the on-orbit self-propelled type harvesting device comprises a cutter device, a pushing-gathering device, a transmitting device, a tensioning device, a travelling device, a horizontal lifting mechanism, a control part and a storage battery, wherein the cutter device is used for harvesting leaf vegetables in a working area; the pushing-gathering device is used for pushing the harvested leaf vegetables to a conveyor belt; the transmitting device is used for transmitting the harvested leaf vegetables to a vegetable basket; the tensioning device is used for tensioning the conveyor belt in an operation process; the travelling mechanism is used for driving the whole machine to move on a parallel rail; the control part is used for controlling each motor to operate at proper time. According to the on-orbit self-propelled type harvesting device, self-propelled travelling on the parallel rail in the leaf vegetable harvesting process is realized without manually interfering a travelling direction and forwarding speed of the harvester, so that the harvesting efficiency is high and the labor power is saved. Cutters not only can regulate height above the ground, but also can be kept in a horizontal cutting gesture all the time; a rope section type pushing-gathering machine can be used for collecting, cutting and pushing the leaf vegetables to the conveyor belt under the condition of not damaging the leaf vegetables. Two cutters which oppositely move back and forth are adopted, so that efficient cutting is realized.

On-orbit self-propelled type harvesting device for greenhouse leaf vegetable harvesting robot and harvesting method

The invention discloses a wisdom water conservancy management-control cloud platform and method. The wisdom water conservancy management-control cloud platform comprises a data acquisition terminal, a cloud terminal, an intelligent control terminal and an intelligent execution terminal; the data acquisition terminal acquires field water volume information, crop growth information and pump station water source information, and sends the acquired field water volume information, the acquired crop growth information and the acquired pump station water source information to the cloud terminal; the cloud terminal stores appropriate soil moisture thresholds of various growth periods of various crops, the cloud terminal makes an irrigation scheme according to the appropriate soil moisture thresholds, weather information, the field water volume information, the crop growth information and the pump station water source information, and the made irrigation scheme is sent to the intelligent control terminal; and the intelligent control terminal controls the intelligent execution terminal according to the irrigation scheme to achieve intelligent irrigation, the intelligent irrigation makes full use of water conservancy information resources, and water conservancy irrigation engineering construction and water affair treatment efficacy and benefits are improved.

Wisdom water conservancy management-control cloud platform and method

The invention relates to a system and method for integrally and intelligently controlling water and fertilizer in a field based on multi-source information fusion. The system comprises a weather forecast inquiry receiving subsystem, a weather real-time data collecting control subsystem, a cloud computing platform, a central control unit, an irrigation and fertilization control subsystem, an irrigation and fertilization state monitoring system and an online fault detection system. The system provided by the invention is an automatic control system integrated with the functions of weather forecast inquiry, crop cloud computing platform inquiry, farmland weather real-time collection, solid fertilizer rapid solution, mother solution real-time monitoring regulation, irrigation and fertilization state monitoring, online fault detection, irrigation and fertilization and remote intelligent control. According to the invention, the factors, such as, weather forecast, cloud computing platform, weather real-time collection and growth vigor of the crops in the growth process can be comprehensively considered, corresponding irrigation and fertilization decisions can be made, and thus precise irrigation and precise fertilization can be accurately realized; and the growth vigor of the crops can be described in real time, and the irrigation and fertilization can be performed according to the growth vigor of the crops.

System and method for integrally and intelligently controlling water and fertilizer in field based on multi-source information fusion

The invention relates to a monorail plant protection robot for a greenhouse and a control method thereof. The monorail plant protection robot comprises a guide track, a drive system, a mist spray system, a mist spray posture adjustment system, a control system and a power supply source. The guide track is used for supporting the drive system and achieves guiding and auxiliary locating functions of the drive system. The drive system is used for supporting the mist spray system, the mist spray posture adjustment system and the control system to advance, retreat or stop along a monorail track. The mist spray system is used for carrying out mist spraying operation on crops. The control system is used for accurately locating the plant protection robot in the greenhouse, giving a control signal for moving or stopping the plant protection robot, controlling the mist spray system to adjust mist spray postures and changing mist spraying modes. The operation motion of the plant protecting robot is accurately controlled, for different planting modes and different growth periods of vegetables, corresponding mist spraying operation modes are changed, and the full-automatic precision mist spray function is achieved; meanwhile, intelligent feedback control is adopted, and therefore the plant protection robot is good in safety and high in automation degree and working efficiency, and saves labor.

Li Yang

Papers

System and method for integrally and intelligently controlling water and fertilizer in field based on multi-source information fusion

On-orbit self-propelled type harvesting device for greenhouse leaf vegetable harvesting robot and harvesting method

Monorail plant protection robot for greenhouse and control method thereof

Recognition of cotton growth period for precise spraying based on convolution neural network

Spraying strategy optimization with genetic algorithm for autonomous air-assisted sprayer in Chinese heliogreenhouses