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

Research on the constitutive model of low-damage corn threshing based on DEM

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 opportunity of using of Unmanned Aerial Vehicles (UAVs) for applying harvest aids in ultra-low volumes (ULV) in cotton is an interesting perspective to overcome common problems associated with traditional ground-based applications like crop mechanical damage, yield losses and soil compaction. Moreover, as cotton harvest aids induce a prominent effect on the appearance of the crop, their efficacy can be conceptualized in wide scale remote sensing observations to provide general insights on the performance of UAV plant protection applications. The scope of the present study was to evaluate the efficacy of UAV spraying in cotton in comparison with traditional ground-based applications. Two field trials were established for that scope and each field included eight treatments. Two treatments involved ground based, field sprayer applications with and without air assistance on the boom and six treatments, combinations of multirotor UAV applications involving two spray altitudes, (2 m and 3 m above the canopy), two spray volumes (16 l ha −1 and 10 l ha −1 ) and the addition of a spray adjuvant on the mixture. The efficacy of the alternative treatments was evaluated through UAV remote sensing and by ground truth measurements. The evaluated parameters were the differences obtained in the Normalized Difference Vegetation Index - NDVI and the Cotton Fiber Index – CFI, from time of spraying to harvest, the defoliation rate, the boll opening rate and the final yield. The results revealed that UAV sprayings were more efficient from both field sprayer applications (with and without air assistance). The higher efficacy was accompanied by an improvement in cotton yield. The best results were obtained from the low altitude, 2 m UAV operation. There were no clear differences between the high volume and the low volume UAV applications implying that the lower 10 l ha −1 volume might be sufficient and provide the potential to increase field capacity. The addition of the spray adjuvant was beneficial only when spraying was followed by rainfalls. The results of the present study implicate that UAV applications can support a wide range of plant protection and plant care applications in cotton. 

Xuemei Liu

Papers

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

Estimation model of canopy stratification porosity based on morphological characteristics: A case study of cotton

Burgers viscoelastic model-based variable stiffness design of compliant clamping mechanism for leafy greens harvesting

Predicting spray deposit distribution within a cotton plant canopy based on canopy stratification porosity and Gaussian process models