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To improve the innovation of agricultural machinery product styling, this paper proposes a shape structure behavior function (SSBF) model suitable for the industrial design field. The feature line evolution method combining shape grammar and genetic algorithm was used for modelling the of the grader, which not only maintains the product style characteristics but also reflects the typical identification characteristics of the bionic prototype and produces a new product modelling scheme. By conducting cognitive and recognition experiments on product styling features, the ranking of product styling features and the contribution of each component to product styling were determined. The method of combining shape grammar and quadratic Bézier curve was used to express and encode feature lines, and genetic algorithm was used to evolve biomimetic forms to form product feature lines with typical biological morphological features; The extracted form bionic elements were integrated into the grader modelling design, and the interaction evaluation was carried out through the genetic algorithm evolution scheme. The basic form elements were extracted and analyzed, and the deduction rules were formulated and reorganized. The derived feature line geometric data considered the product's image features and the bio-inspired prototype, which can be used for the follow-up guidance of industrial design schemes. [ABSTRACT FROM AUTHOR]

During the field application of pesticides by plant protection UAV, due to the interference of meteorological factors or operating parameters, the deposition effect of droplets is poor. In order to explore the impact of the nozzle angle of plant protection UAV on droplet deposition distribution, this article is based on field experiments and collects data on different flight speeds (1, 3, 5 m/s) and nozzle angles (±60°, ±45°, ±30°, 0°), and performs variance analysis and regression analysis on the test results. The results showed that adjusting the nozzle angle had a significant effect on the amount of droplet deposition and deposition uniformity. Compared with 0°, the nozzle angle of -30° increased the amount of deposition by 76.94% and 61.04% at flight speeds of 1.2 m/s and 3 m/s, respectively. The flight speed had a significant effect on the amount of droplet deposition, and the increase in flight speed decreased the amount of droplet deposition by 55.97%-77.06% and had no significant effect on the uniformity of droplet deposition. This study provides a reference for improving the droplet deposition effect of plant protection UAV field pesticide application operations. [ABSTRACT FROM AUTHOR]

With the gradual deterioration of the ecological environment and the increase in requirements for the quality of modern life, the use of pesticides is bound to develop towards higher pesticide utilization and less environmental pollution, and the low-volume spraying for agricultural aviation operation combined with the Drift Reducing Technologies (DRTs) may be a useful way to achieve this goal. Based on an analysis of the spray drift mechanism and the primary factors influencing aerial spraying, previous research on DRTs in aerial spraying were reviewed and summarized, and it was found that DRTs in aerial spraying can effectively reduce the environmental pollution caused by pesticide drift by reducing the spraying amount of pesticides and improving the control effect of pesticides, included aerial electrostatic spray technology, aerial spray adjuvant, aerial air-assisted spray technology, drift reducing nozzles and aerial variable-rate spray technology. And according to the analysis of the current research status, some suggestions and countermeasures to reduce droplet drift of agricultural aviation spraying were put forward from the aspects of strengthening the research on DRTs for plant protection Unmanned Aerial Vehicle (UAV) and adopting reasonable DRTs methods. It is hoped that provide reference and guidance for the enterprises’ product improvement and users’ practical operation, and play the advantages of precision agricultural aviation spraying fully [ABSTRACT FROM AUTHOR]

With changing climate and farmland ecological conditions, pest outbreaks in agricultural landscapes are becoming more frequent, increasing the need for improved crop production tools and methods. UAV-based agricultural spraying is anticipated to be an important new technology for providing efficient and effective applications of crop protection products. This paper reviews and summarizes the status of the current research and progress on UAV application technologies for plant protection, and it discusses the characteristics of atomization by unmanned aircraft application systems with a focus on spray applications of agrichemicals. Additionally, the factors influencing the spraying performance including downwash airflow field and operating parameters are analyzed, and a number of key technologies for reducing drift and enhancing the application efficiency such as remote sensing, variable-rate technologies, and spray drift models are considered. Based on the reviewed literature, future developments and the impacts of these UAV technologies are projected. This review may inspire the innovation of the combined use of big data analytics and UAV technology, precision agricultural spraying technology, drift reduction technology, swarm UAV cooperative technology, and other supporting technologies for UAV-based aerial spraying for scientific research in the world. [ABSTRACT FROM AUTHOR]

In order to solve the problem of insufficient adsorption rate of droplets on the target back via aerial electrostatic spray, this study proposed a high-voltage electrostatic generator to charge the liquids in two isolated water tanks with positive and negative charges respectively. A charge transfer loop was developed in space between the aerial electrostatic spray system and the ground. This method greatly enhanced the adsorption performance under outdoor conditions that 16.7% droplets density increased on the target front, a nearly fourfold destiny increased on the target back compared with the conventional UAV spray system. The target back-to-front ratio of droplet density was improved from 6.1% to 25.7%, which validated the satisfactory performance of the developed system. [ABSTRACT FROM AUTHOR]

The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture. UAV remote sensing has the advantages of low cost use, simple operation, real-time acquisition of remote sensor images and high ground resolution. It is difficult to separate cultivated land from other terrain by using only a single feature, making it necessary to extract cultivated land by combining various features and hierarchical classification. In this study, the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information, shape information and position information of farmland. Based on the vegetation index, texture information and shape information in the visible light band, the object-oriented method was used to study the best scheme for extracting cultivated land area. After repeated experiments, it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters. Uncultivated crops and other features are separated by using the band information and texture information. The overall accuracy of this method is 86.40% and the Kappa coefficient is 0.80. The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision. However, there are some cases where the finely divided plots are misleading, so further optimization and improvement are needed. [ABSTRACT FROM AUTHOR]

The rapid developments of unmanned aerial vehicles (UAV) and vision sensor are contributing a great reformation in precision agriculture. Farmers can fly their UAV spraying pesticides around their crop fields while staying at their remote control room or any place that is separated from their farm land. However, there is a common phenomenon in rice planting management stage that some empty areas are randomly located in farmland. Therefore, a critical problem is that the waste of pesticides that occurs when spraying pesticides over rice fields with empty areas by using the common UAV, because it is difficult to control the flow accuracy based on the empty areas changing. To tackle this problem, a novel vision-based spraying system was proposed that can identify empty areas automatically while spraying a precise amount of pesticides on the target regions. By this approach, the image was preprocessed with the Lucy-Richardson algorithm, then the target area was split from the background with k-means and the feature parameters were extracted, finally the feature parameters were filtered out with a positive contribution which would serve as the input parameters of the support vector machine (SVM) to identify the target area. Also a fuzzy control model was analyzed and exerted to compensate the nonlinearity and hysteresis of the variable rate spraying system. Experimental results proved that the approach was applicable to reducing the amount of pesticides during UAV spraying, which can provide a reference for precision agriculture aviation in the future. [ABSTRACT FROM AUTHOR]

The variable-rate application is an important aspect of precision agriculture. In order to determine the regular patterns of droplet deposition and compare the actual variable-rate spraying effect of the AS350B3e helicopter with the AG-NAV Guía system, spray tests were conducted with different operating parameters and operating methods. In this paper, the deposition distribution of droplets in the effective swath area was evaluated for six single-pass applications at four different flight velocities. The effects of adding adjuvant on droplet deposition, drift and droplet size were compared, and the actual variable effect of the forth-back application was verified. The analysis results showed that the position of the effective swath area was affected by natural wind velocity and wind direction, and would shift to the downwind direction area from the helicopter route of a different degree. The effective swath width increased slowly and then decreased sharply with the increase of flight velocity. It was found that flight velocity of 100 km/h was the peak inflection point of effective spray width variation. Moreover, the effect of flight velocity on the distribution uniformity of droplet deposition in the effective swath area was not significant. In the single-pass application of 90 km/h, adding adjuvant could increase droplet size in the effective swath area. The deposition increased by 8.98%, and the total drift decreased by 28.65%, of which the upwind drift decreased by 28.31% and the downwind drift decreased by 29.06%. In the forth-back application of 90 km/h, the error between actual application volume and system setting dose was 12%. The results of this study can provide valuable references for future research and practices on variable-rate aerial applications by manned helicopters. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the wind field disturbs the trajectory of falling seeds and causes the seeds to be unable to be arranged in equidistant rows when the UAV is spreading rice, a shot seeding device that can sow five rows of pelleted rice seeds at the same time was designed. The unit is centered on an external grooved wheel seed metering device and a seed acceleration unit for row seeding and hole sowing. The airflow simulation of the rotor wind field of the UAV was carried out by simulation software to explore the changes in the wind field of the UAV during operation. The position where the wind field disturbance is minimized is chosen for the seed guide tube arrangement. The position with the least wind field disturbance is chosen to arrange the seed guide tube and combine it with a seed acceleration device to reduce the influence of the UAV wind field airflow on the direction of seed movement. The operational effectiveness of the seeding device with and without wind was verified by an indoor test and an outdoor flight seeding test, respectively. Simulation results show that: when the mouth of the seed guide pipe is 0.9 m away from the paddle, the wind field has the smallest influence on the sowing results. The results of the bench test show that: when the rotational speed is 30-45 r/min, the coefficient of variation of the discharge rate of each row (CVR) and total seed discharge rate stability (CVT) are less than 1.98% and 0.84%, and the seed breakage rate is less than 0.95%, which all conform to the UA V fly sowing industry standards. The outdoor mud box test shows that: when the baffle angle changes by 26%, 58%, and 71%, each slot wheel hole can store 5-10, 3-5 and 1-3 seeds respectively, and the UAV operates at a speed of 2 m/s-3 m/s, the pass rate of hybrid rice is greater than 86%, which meets the agronomic requirements of rice sowing operation. [ABSTRACT FROM AUTHOR]

Spray drift has always been a focus research area in the field of unmanned aerial vehicle (UAV) application. Under the fixed premises of UAV operating parameters, such as height, speed and spraying liquid, the droplet drift is mainly affected by meteorological conditions. In this research, the spray drift and deposition tests were conducted using a QuanFeng120 UAV in a pineapple field under various different meteorological conditions. The experiment results showed that with the changes of UAV operating height and wind speed, the start position of the in-swath deposition area had changed 4 m in the extreme situation. The percentage of the total spray drift was from 15.42% to 55.76%. The position of cumulative spray drift that accounted for 90% of the total spray drift was from 3.70 m to 46.50 m relative to the flight line. According to the downwind spray drift curve, the nonlinear equations of the same type under the four operating conditions of the UAV were fitted. The spray drift and the deposition of UAV application were significantly affected by different meteorological conditions and UAV operating heights. The results could provide a theoretical basis for UAV spraying in pineapple plants and support for spray drift control and prediction. [ABSTRACT FROM AUTHOR]

Small unmanned aerial vehicles (UAVs) have been widely used in different aspects of modern farming management, including pest and disease control in China in recent years. In this study, the spray performance of a small plant protection UAV at low volume spray was evaluated by adjusting the pesticide dosage and adding aerial spraying adjuvants. Droplet deposition, droplet density, coverage, control effect and pesticide residue from field trials were assessed. In addition, the residue and control effect of UAV spray were compared to manual knapsack at high volume spray. The results showed that, the adjuvant applying improved the efficiency of UAV spray. Also, the adjuvant applying reduced the dosage of imidacloprid by 20%. However, there was no significant difference on initial residue between UAV spray and knapsack spray. Thus, plant protection UAV spraying pesticide by adding appropriate adjuvant showed the ability of improving the pesticide effectiveness by improving the control efficiency, reducing the pesticide dosage and residue. [ABSTRACT FROM AUTHOR]

Remote-controlled (RC) unmanned aerial vehicles (UAVs) have been extensively applied in agricultural areas, such as remote sensing, precise spraying pesticides for crop protection, agricultural situation inspection and so on, but these telemanipulated UAVs systems are operated entirely by a ground-based pilot with a need of eyes focus on the remote site UAV flight. The key issue existed in agricultural UAV teleoperation area is a longtime training needed. In this paper, a novel natural UAV teleoperation control system in agricultural application was proposed. In UAV teleoperation scenario, human operator gestures measured by using Kinect sensor can be used as control commands for UAV flight. Moreover, some UAV teleoperation control commands related to human hand gestures were defined, which is similar to the radio gymnastic exercises in China. Therefore, gesture recognition-based UAV teleoperation control is easy to learn and master. In addition, a new real time human hand gesture recognition algorithm was proposed. The stability of UAV flight dynamic of roll, pitch and yaw as well as attitude control were verified with the experiments based on the proposed method. At last, the usability and effectiveness of the proposed method has been verified by the experimental results. [ABSTRACT FROM AUTHOR]

Weed control is necessary in rice cultivation, but the excessive use of herbicide treatments has led to serious agronomic and environmental problems. Suitable site-specific weed management (SSWM) is a solution to address this problem while maintaining the rice production quality and quantity. In the context of SSWM, an accurate weed distribution map is needed to provide decision support information for herbicide treatment. UAV remote sensing offers an efficient and effective platform to monitor weeds thanks to its high spatial resolution. In this work, UAV imagery was captured in a rice field located in South China. A semantic labeling approach was adopted to generate the weed distribution maps of the UAV imagery. An ImageNet pre-trained CNN with residual framework was adapted in a fully convolutional form, and transferred to our dataset by fine-tuning. Atrous convolution was applied to extend the field of view of convolutional filters; the performance of multi-scale processing was evaluated; and a fully connected conditional random field (CRF) was applied after the CNN to further refine the spatial details. Finally, our approach was compared with the pixel-based-SVM and the classical FCN-8s. Experimental results demonstrated that our approach achieved the best performance in terms of accuracy. Especially for the detection of small weed patches in the imagery, our approach significantly outperformed other methods. The mean intersection over union (mean IU), overall accuracy, and Kappa coefficient of our method were 0.7751, 0.9445, and 0.9128, respectively. The experiments showed that our approach has high potential in accurate weed mapping of UAV imagery. [ABSTRACT FROM AUTHOR]

To ensure that a variable-rate spray (VRS) system can perform unmanned aerial spray in accordance with a prescription map at different flight speeds, we examine in this paper such significant factors as the response time of the VRS system and the pressure fluctuation of the nozzle during the variable-rate spraying process. The VRS system uses a lag compensation algorithm (LCA) to counteract the droplet deposition position lag caused by the system response delay. In addition, pulse width modulated solenoid valves are used for controlling the flowrates of the nozzles on the variablerate spray system, and a mathematical model was constructed for the spray rate (L min-1) and the relative proportion of time (duty cycle) each solenoid valve is open. The pressure drop and solenoid valve response time at different duty cycles (50~90%) were measured by indoor experiments. Meanwhile, the lag distance (LD), spray accuracy, and droplet deposition characteristics of the VRS system were tested by conducting outdoor experiments at different flight speeds (4m s-1, 5m s-1, 6m s-1). The results show that LCA can effectively reduce the LD. The LD values of the VRS system with LCA ranged from -0.27 to 0.78m with an average value of 0.32m, while without LCA, the LD values increased to 3.5~4.3m with an average value of 3.87m. The overall spray position accuracy was in the range of 91.56~97.32%. Furthermore, the spray coverage and deposition density, determined using water sensitive paper, were used to evaluate the spray application performance taking into account the spray volume applied. The VRS system can provide the most suitable spray volumes for insecticide and fungicide plant protection products. Based on a prescription map, the optimized VRS system can achieve accurate pesticide spraying as well as desirable spray coverage and deposition density. [ABSTRACT FROM AUTHOR]

The wind field produced by the rotor-wing UAV has a significant impact on the distribution of rice pollen, which directly influences hybrid rice breeding. This research aimed to explore the distribution law of rice pollen in the wind field of small UAV. Aviation Beidou Positioning System UB351 positions all sampling nodes for precise corresponding coordinates and spacing information, and draws UVA's flying trajectories, thus providing accurate data for tests. Wireless sensor network measurement system was used to study the three-direction wind field produced by the rotor-wing UAV under various experimental factors and acquires wind field width and wind speed, and the data were compared with the area ratio and width of pollen distribution. Test of univariate normality was conducted through Shapiro-Wilk test and Kolmogorov-Smimov test. In order to figure out the appropriate flight speed for UAV's pollination under pollen distribution law, it is also necessary to perform analysis of variances on regression model. The comparison of wind speed in longitudinal (X) and lateral (Y) direction show that as the major force of the horizontal wind field produced by the rotor-wing UAV, the wind from Y-direction forms the widest wind field. Moreover, flight speed mainly influences wind field width. To be specific, the width of horizontal wind field decreases as flight speed increases. Meanwhile, UAV flight speed also exerts significant impact on vertical wind field. Both the pollen distribution width of more than 5 pollen grains and the area ratio reached the maximum when the UAV flight was at 4.53 m/s, which was the most favorable speed to pollination. In addition, pollen quantity is closely associated with both horizontal and vertical wind field. With comparison of the pollen quantity of sampling nodes, it was found that the wind field produced by the rotor-wing UAV exerted asymmetrical impact on pollen distribution. Q-Q plot of SPSS verifies that pollen distribution is against normal distribution. The establishment of a multiple linear regression model of pollen distribution and wind speed in three directions indicates that pollen distribution quantity only shares positive linear relation with the wind field in X-direction. These findings provide a theoretical guidance for rice pollination by using agricultural UAVs. [ABSTRACT FROM AUTHOR]

Modern technologies and information tools can be used to maximize agricultural aviation productivity allowing for precision application of agrochemical products. This paper reviews and summarizes the state-of-the-art in precision agricultural aviation technology highlighting remote sensing, aerial spraying and ground verification technologies. Further, the authors forecast the future of precision agricultural aviation technology with key development directions in precision agricultural aviation technologies, such as real-time image processing, variable-rate spraying, multi-sensor data fusion and RTK differential positioning, and other supporting technologies for UAV-based aerial spraying. This review is expected to provide references for peers by summarizing the history and achievements, and encourage further development of precision agricultural aviation technologies. [ABSTRACT FROM AUTHOR]

Wind field is one of the important factors affecting the distribution characteristics of aerial spraying droplet deposition. In order to reveal the impact mechanism of droplet deposition distribution by the wind field below agricultural unmanned helicopter rotor, in this study, the wind field distribution below uniaxial single-rotor electric unmanned helicopter rotor was measured by using a wireless wind speed sensor network measurement system for unmanned helicopter. The effects of wind field in three directions (X, Y, Z) below the rotor on droplet deposition distribution were analyzed with the condition of aerial spraying droplet deposition in rice canopy, and the regression model was established via variance and regression analyses of experiment results. The results showed that, the wind field in Y direction had a significant effect on droplet deposition in effective spray area, the wind field in Z direction had an extremely significant effect on droplet deposition in effective spray area, and the corresponding significance (sig.) values were 0.011 and 0.000. Furthermore, the wind field in Z direction had a significant effect on the penetrability and uniformity of droplet deposition in effective spray area, the corresponding sig. values were 0.025 and 0.011 respectively. The wind speed in Y direction at the edge of effective spray area had a significant effect on droplet drift, the sig. value was 0.021. In addition, the correlation coefficient R of the regression model was 0.869 between droplet deposition in effective spray area and the wind speed in Y and Z directions, and 0.915 between the uniformity of droplet deposition in effective spray area and the maximum wind speed in Z direction. The result revealed the influencing mechanism of the wind field below the rotor of uniaxial single-rotor electric unmanned helicopter on the distribution of aerial spraying droplet deposition. The results can provide guidance for the actual production application of aerial spraying to reduce liquid drift and improve the utilization rate of pesticide. [ABSTRACT FROM AUTHOR]

Citrus Huanglongbing (HLB), which is spread by the citrus psyllid, is the most destructive disease of citrus industry. While no effective cure for the disease has been reported, detection and removal of infected trees can prevent spreading. Symptoms indicative of HLB can be present in both HLB-positive trees and HLB-negative trees, making identification of infected trees difficult. A detection method for citrus HLB based on image feature extraction and two-stage back propagation neural network (BPNN) modeling was investigated in this research. The identification method for eight different classes including healthy, HLB and non-HLB symptoms was studied. Thirty-four statistical features including color and texture were extracted for each leaf sample, following the two-stage BPNN to model and identify HLB-positive leaves from HLB-negative leaves. The discrimination accuracy can reach approximately 92% which shows that this method based on visual image processing can perform well in detecting citrus HLB. [ABSTRACT FROM AUTHOR]

Ground-based platform systems provide a good tool for monitoring and managing crop conditions in precision agriculture applications and have been widely used for monitoring crop conditions. To develop an unmanned ground vehicle system (UGVS) based multi-sensors and test the feasibility of this system for monitoring rice conditions, an UGVS was developed to collect real-time rice condition information including NDVI values, reflectance measurements and crop canopy temperature in this study. Major components of the integrated system are GreenSeeker R100 system, hyper-spectroradiometer and infrared temperature sensor. The leaf area index (LAI) was measured by the CGMD302 Spectrometer. The Independent Samples T-Test method and the one way ANOVA method were used to determine the best spectral indices and analyze the relationship between the vegetation indices and rice LAI. It was found that the two best spectral indices for estimating LAI were NDVI (860 nm and 750 nm) with the correlation coefficient (R²) at 0.745 and RVI (853 nm and 751 nm) with the R² at 0.724. The results show the UGVS can support multi-source information acquisition and is useful for crop management and precision agriculture applications. [ABSTRACT FROM AUTHOR]

Two-spotted spider mites are important pests in many agricultural systems. Spider mites (Acari: Tetranychidae) have been found to cause economic damage in corn, cotton, and sorghum. Adult glass vial bioassays indicate that Temprano™ (abamectin) is the most toxic technical miticide for adult two-spotted spider mite. From an aerial application standpoint, additional research is needed to identiFy aerial application parameters for this miticide. The objective of this study was to investigate spectral response of spider mite-infested cotton plants with different density levels of mites and treated with different rates of miticide. Results showed significantly different spectral signatures of cotton plants infested with different density levels of mites. By treating mite-infested cotton plants with five different Temprano rate treatments (control, one-eighth, one-fourth, one-half, and full rates), spectral reflectance curves were found to be significantly different. Four wavelengths of 550 nm, 560 rim, 680 rim and 740 tim were important for detecting the spectral differences among mite infested cotton plants treated with various rate of Temprano. Normalized Difference Vegetative Index imagery was able to detect different levels of cotton plant damage. Half-rate application of Temprano controlled mite-infested plants as effectively as the full-rate application. These findings may lead to reduced cost and quantity of miticides used to maintain effective crop production and protection. [ABSTRACT FROM AUTHOR]

Variogram plays a crucial role in remote sensing application and geostatistics. It is very important to estimate variogram reliably from sufficient data. In this study, the analysis of variograms computed on various sample sizes of remotely sensed data was conducted. A 100 × 100 - pixel subset was chosen randomly from an aerial multispectral image which contains three wavebands, Green, Red and near-infrared (NIR). Green, Red, NW and Normalized Difference Vegetation Index (NDVI) datasets were imported into R software for spatial analysis. Variograms of these four full image datasets and sub-samples with simple random sampling method were investigated. In this case, half size of the subset image data was enough to reliably estimate the variograms for NW and Red wavebands. To map the variation on NIDVI within the weed field, ground sampling interval should be smaller than 12 m. The information will be particularly important for Kriging and also give a good guide of field sampling on the weed field in the future study. [ABSTRACT FROM AUTHOR]

All parameters in terms of wet alfalfa input capacity, dry alfalfa output capacity, alfalfa stem length, rotary speed, heated air temperature, air flow velocity and number of lifting flights fixed at the interior wall of rotary dryer were integrated and derived into four π terms based on Jiang's principle. The drying temperature was a significant factor influencing the drying rate and the post-drying quality of alfalfa hay. An improved G. Murphy theorem with high confidence was used to design an experimental program for alfalfa drying in a rotary dryer. A π equation was derived by using the multiplication of component equations involving the drying parameters and most of its correlation coefficients were higher than 0.99. The validation results show that most of the relative errors of pterms were lower than 10% and are acceptable in engineering practice. This empirical equation may be used to investigate the parameter interactive effects on conveyor performance, which may be useful in the design and selection of rotary dryers for drying alfalfa hay. [ABSTRACT FROM AUTHOR]

The efficacy of novaluron (Diamond™ 0.83 EC) on cotton, Gossypium hirsutum L., against nymphs of different instars of field-collected southern green stink bug, Nezara viridula (L.), was investigated in a spray chamber using two nozzles, 650033 and 8002E. The nozzles delivered rates of 18.7 and 46.7 L/ha, respectively, of total water-based spray. Novaluron was applied at 0.02 18, 0.0436 and 0.0654 kg active ingredient (Al) rates per ha. The 8002E nozzle produced significantly larger droplets, increased droplet density and increased spray coverage compared to the 650033 nozzle. Regardless of dosage and spray rate, the age of nymphal instars significantly influenced mortality. Mortality of small nymphs (2nd instars) was significantly greater than that of either 3rd- or 4th-instar nymphs. When novaluron was applied at 0.0654 and 0.0872 kg Al per ha with or without crop oil concentrate, the deposit from the 8002E nozzle significantly increased mortality of 3rd-instar nymphs; however, neither dosage nor addition of crop oil concentrate increased mortality of 3rd-instar nymphs. Novaluron did not cause a great amount of mortality of 4th-instar nymphs. This study suggests that application of novaluron should be directed at 2nd-instar nymphs to maximize efficacy for control of southern green stink bug on cotton and that applications that produce deposits with increased droplet size and density on plants might increase efficacy. [ABSTRACT FROM AUTHOR]

The article focuses on the effect of the formal diplomatic relationship of the U.S. and the People's Republic of China on the development and exchange of agricultural engineering professionals. It notes that the continued cooperation between these countries come up with the appropriate solutions to agricultural and environmental issues such as the generation of energy, environmental quality maintenance and feeding of increasing world population. It points out that agricultural and biological engineers are working successfully to make the world a better place.

Lychee, a key economic crop in southern China, has numerous similar-looking varieties. Classifying these can aid farmers in understanding each variety's growth and market demand, enhancing agricultural efficiency. However, existing classification techniques are subjective, complex, and costly. This paper proposes a lychee classification method using an improved ResNet-34 residual network for six common varieties. We enhance the CBAM attention mechanism by replacing the large receptive field in the SAM module with a smaller one. Attention mechanisms are added at key network stages, focusing on crucial image information. Transfer learning is employed to apply ImageNet-trained model weights to this task. Test set evaluations demonstrate that our improved ResNet-34 network surpasses the original, achieving a recognition accuracy of 95.8442%, a 5.58 percentage point improvement. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low correlation between variable fertilizer application system and soil nutrient content detection and insufficient real-time performance, a variable fertilizer application system based on real-time soil nutrient content detection was developed. This paper describes the structure, working principle and design of key components of the soil information acquisition and fertilizer application system. It includes the simulation and analysis of fertilizer application using the discrete element method and the selection of curved blade fertilizer application discs. The system uses STM32F429IGT6 microcontroller and ROS higher-level computer for decision making. The device detects soil nutrients in real time, adjusts the fertilizer motor speed accordingly, and runs autonomously along the planned path. The decision coefficient R2 between the fertilizer application rate and the speed of the fertilizer application wheel is not less than 0.97, and the relative error between the actual fertilizer application rate and the theoretical fertilizer application rate is up to 5.91%, with the maximum value of the coefficient of variation (CV) of 10.18%. The indoor bench test shows that the relative error between the actual fertilizer application rate and the target fertilizer application rate within a single operating grid is up to 6.2%, with the maximum value of CV being 6.64%. The field test in the orchard showed that the maximum relative error between the actual fertilizer application and the target fertilizer application in a single operation grid was 6.3%, and the maximum value of CV was 12.34%, and the fertilizer application was completed in the operation grid, which demonstrated that the device was able to realize real-time and accurate variable fertilizer application according to the soil nutrient information. [ABSTRACT FROM AUTHOR]

The aim of this research is to address the issues of low precision in variable spraying within the existing farmland application system, whereby nozzles cannot be controlled independently and low pesticide utilisation is observed. A variable spraying system based on PTO protocol has been designed. The system comprises an STM32 microcontroller as the control core, including the host computer, multi-channel controller, electronic switch, and electromagnetic spray nozzle. The master control unit receives the spray amount set by the user and calculates the duty cycle of the corresponding nozzle, which is then sent to the multiplex controller in real time through CAN communication. The multiplex controller adjusts the on-off frequency and duty cycle of each electromagnetic nozzle in real time according to the duty cycle of the corresponding nozzle it receives, thus enabling the nozzles to be controlled independently. This study offers theoretical and technical support for the independent control of spray nozzles and the optimisation of pesticide utilisation for variable spray systems based on PTO protocols. [ABSTRACT FROM AUTHOR]

Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection. [ABSTRACT FROM AUTHOR]

Precision soil fertilization is an important aspect of smart precision agriculture development, and the fertilization prescription map is a prerequisite for precision fertilization. Taking grapevine soil information as an example, this study explores the impact of different sampling densities on the accuracy of soil nutrient distribution. Experimental trials were conducted using sampling densities of 1m x 1m, 3m x 3m, 6m x 6m, 9m x 9m, and 12m x 12m, with the optimal sampling density determined to be 6m x 6m. Nutrient distribution maps were created using Bigemap and ArcGIS software, and based on nutrient balance calculations using ArcGIS software, fertilization prescription maps were developed. Furthermore, precise fertilization schemes for nitrogen, phosphorus, and potassium fertilizers were formulated based on the prescription maps. This study provides methodological and data support for research on precision soil fertilization. [ABSTRACT FROM AUTHOR]

The more information obtained about the driving environment, the more ensures driving safety. Due to the complex driving environment of farmland roads, targets beside the road sometimes have an important impact on driving safety. To achieve this goal, a novel real-time detection and prediction algorithm of targets was proposed. The whole image was divided into four parts by RCM: driving region, crossroad region, roadside region, and the other region. In addition, a safety policy for every part was enforced by the algorithm, which was based mainly on the combination of the YOLACT and GPM. On this basis, a self-collected data set of 5000 test samples is used for testing. The detection accuracy of the algorithm in the data set could reach up to 90%, and the processing speed to 30.4 fps. In addition, experiments were carried out on actual farmland roads, and the results showed that the proposed algorithm was able to detect, track, and predict targets on the farmland road, and alarm to driver in time before the targets rush into the road. This study provides an important reference for the safe driving of agricultural vehicles. [ABSTRACT FROM AUTHOR]

Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. [ABSTRACT FROM AUTHOR]

During the process of plant protection in agriculture, the distribution and deposition of droplets or fog fields could directly influence the effectiveness and efficiency of spray. The traditional method of measurement of the distribution of droplets mainly used water sensitive papers, glass containers or flour to collect data and inverse results, while a new method of measurement based on the principle of reflection of LIDAR was presented. Droplets were the major targets of the study, and four important algorithms were primarily developed, including the recognition and extraction of targets, the superposition in time-domain, the calculation of effective ranges of distribution, and the development of 3D distribution models. Combined with these algorithms, in order to eliminate the environmental noise, the methods of Fuzzy Environment Matching and Secondary Filter were created and utilized. Meanwhile, the statistics was used for analysis of the duration of scanning as well as computation of the distribution, with enough datasets but the minimum length of time. The results of the experiments showed that the relative error of measurement was less than 7% and Relative Standard Deviation was less than 16%, compared with the values of manual measurement. Furthermore, the 3D models were accurate and clarified in the wind-tunnel experiment. The completed system based on this method could adapt to the requirements of both indoor and outdoor detection. Besides, it is capable of the quantized detection of droplet distribution, providing an effective way of tests for spray technique, especially for the research of the application of plant protection by UAVs. [ABSTRACT FROM AUTHOR]

Spray characteristics are the fundamental factors that affect droplet transportation downward, deposition, and drift. The downwash airflow field of the Unmanned Aviation Vehicle (UAV) primarily influences droplet deposition and drift by changing the spray characteristics. This study focused mainly on the effect of the downwash airflow field of the UAV and nozzle position on the droplet spatial distribution and velocity distribution, which are two factors of spray characteristics. To study the abovementioned characteristics, computational fluid dynamics based on the lattice Boltzmann method (LBM) was used to simulate the downwash airflow field of the DJI T30 six-rotor plant protection UAV at different rotor rotational speeds (1000-1800 r/min). A particle image velocimetry system (PIV) was utilized to record the spray field with the downwash airflow field at different rotational speeds of rotors (0-1800 r/min) or different nozzle positions (0, 0.20 m, 0.35 m, and 0.50 m from the motor). The simulation and experimental results showed that the rotor downwash airflow field exhibited the ‘dispersion-shrinkage-redispersion’ development rule. In the initial dispersion stage of rotor airflow, there were obvious high-vorticity and low-vorticity regions in the rotor downwash airflow field. Moreover, the low-vorticity region was primarily concentrated below the motor, and the high-vorticity region was mainly focused in the middle area of the rotors. Additionally, the Y-direction airflow velocity fluctuated at 0.4-1.2 m under the rotor. When the rotor airflow developed to 3.2 m below the rotor, the Y-direction airflow velocity showed a slight decrease. Above 3.2 m from the rotor, the Y-direction airflow velocity started to drastically decrease. Therefore, it is recommended that the DJI T30 plant protection UAV should not exceed 3.2 m in flight height during field spraying operations. The rotor downwash airflow field caused the nozzle atomization angle, droplet concentration, and spray field width to decrease while increasing the vortex scale in the spray field when the rotor system was activated. Moreover, the increase in rotor rotational speed promoted the abovementioned trend. When the nozzle was installed in various radial locations below the rotor, the droplet spatial distribution and velocity distribution were completely different. When the nozzle was installed directly below the motor, the droplet spatial distribution and velocity distribution were relatively symmetrical. When the nozzle was installed at 0.20 m and 0.35 m from the motor, the droplets clearly moved toward the right under the induction of stronger rotor vortices. This resulted in a higher droplet concentration in the right-half spray field. However, the droplet moved toward the left when the nozzle was installed in the rotor tip. For four nozzle positions, when the nozzle was installed at 0 or 0.20 m from the motor, the droplet average velocity was much higher. However, the droplet average velocity was slower when the nozzle was installed in the other two positions. Therefore, it is recommended that the nozzle is installed at 0 or 0.20 m from the motor. The research results could increase the understanding of the downwash airflow field distribution characteristics of the UAV and its influence on the droplet spatial distribution and velocity distribution characteristics. Meanwhile, the research results could provide some theoretical guidance for the choice of nozzle position below the rotor. [ABSTRACT FROM AUTHOR]

A conventional spraying mode and a fully autonomous fruit tree operation mode using a model DJ T30 unmanned aerial vehicle (UAV) were used to control aphids control on elm trees and to clarify the distribution of droplets in elm trees sprayed by an UAV. The effects of six aviation spray adjuvants on elm canopy droplet deposition and aphid control were evaluated. ImageJ software was used to analyze and measure the droplet density and deposition of water sensitive paper in two modes; this was done to calculate the droplet uniformity, depositional penetration and droplet penetration, and to verify the aphid control effect. The results showed that the droplet density increased by 79.7%-100.7% in the upper canopy and 0-394.1% in the lower canopy without adjuvants in the fully autonomous fruit tree operation mode. The upper canopy deposits increased by 65.7%-179.3%, and the lower canopy increased by 0-152.8%. When adjuvants were added, the droplet density in the upper canopy increased by 49.7-56.1% using Jiexiaofeng (JXF), and the lower canopy increased by 138.2%-177.8% using JXF, 45.8%-141.3% using Beidatong (BDT), 45.5%-92.9% using Gongbei (GB), 0-93.5% using Maisi (MS), and 0-95.2% using Manniu (MN). The deposits of the upper canopy increased by 888.1-1154.2% using JXF, 0-1298.3% using MN, 0-343.9% using BDT, 0-422.5% using GB, 0-580.3% using MS. The lower canopy increased by 746.4%-1426.0% using JXF, 226.2%-231.0% using BDT, 435.8%-644.0% using GB, 255.0%-322.4% using MS, and 249.3%-360.0% using MN. When JXF was added, the droplet uniformity, droplet penetration and depositional penetration were better than when using other adjuvants. The effects of JXF, BDT and GB in controlling aphids was significantly better than other adjuvants (p<0.05). The following control effects were observed; 94.1% with JXF, 93.1% with BDT, and 93.3% with GB after 3 d of application, and 97.9% with JXF, 95.6% with BDT, and 97.1% with GB after 7 d of application. At the same time, the application of the fully autonomous fruit tree operation mode and JXF can effectively improve the density and deposits, which will produce a superposition optimization effect. Our study focuses on the prevention and control of elm aphid infestations based on the operation mode of a UAV and aviation spray adjuvants, which can provide a baseline for the control of diseases and insect pests using UAVs in agriculture and forestry. [ABSTRACT FROM AUTHOR]

Leaf Area of Index (LAI) refers to half of the total leaf area of all crops per unit area. It is an important index to represent the photosynthetic capacity and biomass of crops. To obtain LAI conditions of summer maize in different growth stages quickly and accurately, further guiding field fertilization and irrigation. The Unmanned aerial vehicles (UAV) multispectral data, growing degree days, and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion. The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands. The correlation analysis was conducted to verify the accuracy of the multispectral data. To include many bands as possible, four vegetation indices which included R, G, B, and NIR bands were selected in this study to test the spectral accuracy. There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band. Through correlation analysis of LAI and the vegetation index, vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model. In addition, the Canopy Height Model (CHM) and Growing degree days (GDD) of summer maize were also calculated to build the LAI inversion model. The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model (GLR), Multivariate nonlinear regression model (MNR), and the partial least squares regression (PLSR) models. R² and RMSE were used to assess the accuracy of the model. The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64, which was significant. The Wide Dynamic Range Vegetation Index (WDRVI), Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Plant Biochemical Index (PBI), Optimized Soil-Adjusted Vegetation Index (OSAVI), CHM and GDD have a higher correlation with LAI. By comparing the models constructed by the three methods, it was found that the PLSR has the best inversion effect. It was based on OSAVI, GDD, RVI, PBI, CHM, NDVI, and WDRVI, with the training model’s R² being 0.8663, the testing model’s R² being 0.7102, RMSE was 1.1755. This study showed that the LAI inversion model based on UAV multispectral vegetation index, GDD, and CHM improves the accuracy of LAI inversion effectively. That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly, and this method can provide decision support for maize growth monitoring and field fertilization. [ABSTRACT FROM AUTHOR]

Objects in agricultural soils will seriously affect the farming operations of agricultural machinery. At present, it still relies on human experience to judge abnormal Gounrd-penetrting Radar (GPR) signals. It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil. Although many scholars had researched a variety of image-processing techniques, most methods lack robustness. In this study, the deep learning algorithm Mask Region-based Convolutional Neural Network (Mask-RCNN) and a geometric model were combined to improve the GPR positioning accuracy. First, a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves. Secondly, a SOIL-GPR geometric model was proposed, which can be combined with Mask-RCNN's MASK geometric size to predict object sizes. The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils; then, the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks (FCN); Finally, the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment. The detection accuracy was evaluated by several indicators, such as the survey line direction, soil depth false alarm rate, Mean Average Precision (mAP), and Intersection over Union (IoU). The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively, and the average error of depth prediction is 2.87 cm. The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters, geometric models, and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils. [ABSTRACT FROM AUTHOR]

With the development of Unmanned Aerial Vehicle (UAV) sprayers, the application of low-volume spraying of harvest-aid and other agrochemicals to cotton using UAVs is becoming a new agronomic trend worldwide. The effect of spray volume and canopy density for UAV spraying is significant but was rarely studied. In this study, five representative spray volumes were explored to examine the effect of spray volume on deposition and harvest-aid efficacy for cotton using a UAV sprayer. To explore the effect of canopy density, similar tests were carried out in a field located nearby with a lower leaf area index (LAI). A conventional trailer boom sprayer was selected for comparison. Different spray volumes had a significant effect on defoliation, but had no significant effect on boll opening and fiber quality. A higher defoliation rate was achieved in the lower LAI field. The total rate of defoliation using the UAV was inferior to the boom sprayer in the high LAI field for lower deposition and defoliation rate in the lower canopy. Considering the deposition, defoliation rate, and working efficiency, a spray volume of 15.0 L/hm2 with an average droplet size of 150 µm is recommended for UAV application. [ABSTRACT FROM AUTHOR]

In recent years, multi-rotor Unmanned Aerial Vehicles (UAVs) have been employed in the field of plant protection in China. Spray drift has been considered a major impact in agriculture aerial spraying, and spray quality in the application of plant protection products. The downwash including wake vortices and downward wind field plays a major role in the dispersal and deposition of pesticide spray released by nozzle(s) equipped in aircraft. Differ from the fixed-wing UAV, the downwash flow of multi-rotor UAV resulted from the rotation of the rotor. Therefore, a study on off-target drift and ground deposit concerning the rotor rotation was simulated through a series of Computational Fluid Dynamics (CFD) simulations to obtain the influence of downwash. The discrete Phase Model (DPM) was taken to simulate the motion of droplet particles since it is an appropriate way to simulate discrete phases in the flow field and can track particle trajectory. In this study, the parameters of CFD simulations were acquired by three kinds of actual replicated experiments. The simulation analysis mainly obtains the droplet drift and deposition rule, the influence of eddy current, and downwash flow caused by the rotor rotation. The results showed that the downwash distribution below different rotors was different owing to the flight angle of inclination, "behind" is the greatest, "middle" is secondly, and "forward" is the smallest in value(behind, middle, and forward represent three regions below rotors along flight direction). According to the simulation results, two methods of reducing droplet drift were put forward and specific simulations were carried out to prove their feasibility. The results of this study can provide theoretical support for improving the spray quality of UAVs and reducing the drift of droplets. [ABSTRACT FROM AUTHOR]

The effects of external factors such as temperature, humidity, pesticide formulation, and pesticide concentration on the contact angle of pesticide droplets on rice leaf surfaces were analyzed. The experiments showed that there were significant differences in the contact angles of droplets on the leaf surfaces under different temperatures and humidity. As the ambient temperature increased, the contact angle first decreased and then increased, reaching a minimum value at 25°C. With a gradual increase in humidity, the contact angle significantly increased and reached a maximum at 100% humidity. Finally, it was concluded that both the formulation and concentration of the pesticide had a significant effect on the contact angle of droplets on rice leaf surfaces. The experiments also illustrated that the effects of the pesticide formulation and concentration on the contact angle were more significant than those of temperature and humidity. [ABSTRACT FROM AUTHOR]

This study investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production. The three systems have been used in agricultural studies. They range from low-cost to relatively high-cost, manually operated to automated, multispectral composite imaging with a single camera and integrated imaging with custom-mounting of separate cameras. Practical issues regarding use of the imaging systems were described and discussed. The low-cost system, due to band saturation, slow imaging speed and poor image quality, is more preferable to slower moving platforms that can fly close to the ground, such as unmanned autonomous helicopters, but not recommended for low or high altitude aerial remote sensing on fixed-wing aircraft. With the restriction on payload unmanned autonomous helicopters are not recommended for high-cost systems because they are typically heavy and difficult to mount. The system with intermediate cost works well for low altitude aerial remote sensing on fixed-wing aircraft with field shape-file-based global positioning triggering. This system also works well for high altitude aerial remote sensing on fixed-wing aircraft with global positioning triggering or manually operated. The custom-built system is recommended for high altitude aerial remote sensing on fixed-wing aircraft with waypoint global positioning triggering or manually operated. [ABSTRACT FROM AUTHOR]

The thermomechanical property of the rice kernels was investigated using a dynamic mechanical analyzer (DMA). The length change of rice kernels with a loaded constant force along the major axis direction was detected during temperature scanning. The thermomechanical transition occurred in rice kernels when heated. The transition temperatures were determined as 47°C, 50°C and 56°C for the medium-grain rice with the moisture contents of 18.1%, 16.0% and 12.5% (wet basis), respectively. Length change of the rice kernels increased with the increase of the temperature and moisture content. Among the four rice varieties investigated, the results showed that the thermomechanical property was not significantly affected by variety. [ABSTRACT FROM AUTHOR]

In this paper, the vertical disk mechanical seed-metering device is taken as the object to optimize the design of seed holding ring. The laboratory test is carried out with seed-metering test bench and high-speed camera system; and the influence law of starting and ending angle, front inclination angle and flexible buffer of seed holding ring on the seed filling performance is studied. The ending angle of seed holding ring shall be consistent with seed throwing angle of seed-metering device, and the starting angle of seed holding ring has a significant effect on seed filling performance. The quality of fill index will first increase and then decrease, reaching the maximum at 35.7°; while miss fill index will first decrease and then increase, and the multiple fill index will be 0% when the starting angle is greater than 50°; and seed damage index will first decrease and then increase. In order to improve seed filling performance, the front end of seed holding ring is treated with inner chamfering. The larger the angle is, the lower the quality of fill index, the higher the multiple fill index and the lower the miss fill index, but the seed damage index will also increase. By setting a flexible buffer in the front end of seed holding ring, the seed damage index can be effectively reduced. When the buffer is 60 mm, the seed can be cleaned smoothingly, and the seed damage index will be 0%. [ABSTRACT FROM AUTHOR]

The vertical disc seed metering device was selected to study the influence of the structural characteristics of seed group on the filling performance of seed metering device. The working process of seed metering device was simulated by discrete element software to study the influence of seed quantity and structure characteristics of seed chamber on seed movement and filling results. The seed group was divided into four zones: ascending zone, relative static zone, collapse zone and recirculation zone. The number of seeds and the rotation speed of disk have no effect on the inclination angle of the recirculation zone upper surface, which was always about 35°. But they had a significant effect on the filling performance. When the number of seeds was 500-1500, QFI (quality of feed index) decreased, MIS (miss index) increased and MUL (multiple index) did not change significantly. However, when the number of seeds was 2000, the change trend of filling performance was completely opposite. Inner surface inclination angle and axial width of the seed chamber have significant effects on the filling performance. QFI first increased and then decreased with the increase of inclination angle, reaching the maximum at 20° and MIS showed the opposite trend. QFI increases with the increase of thickness and tends to be stable at 40mm. Similarly, MIS has the opposite trend. [ABSTRACT FROM AUTHOR]

Waterlogging in the early stage of cotton will reduce the number of bolls and do harm to yield. Early detection of waterlogging will help farmers to adjust cotton management and save the loss. To evaluate the application of deep learning for the detection of early waterlogging, this study applied a convolutional neural network (CNN) to classify different durations of waterlogging stress (0, 2, 4, 6, 8, 10 d) based on hyperspectral images (HSIs) of cotton leaves. An experiment was designed to simulate the situation of cotton under waterlogging stress and collect HSIs of visible and near-infrared (VNIR 450-950 nm) spectra with 126 bands 66 d after cotton sowing (66 DAS). It was found the spectral curve reflectance of waterlogging cotton was higher than that of non-waterlogging cotton. Especially near 550 nm and 750 nm, and the spectral curve increased with durations of waterlogging stress and there were 'blue shift' phenomena for the position of the red edge of the spectra. The first principal components of HSIs after band randomly discarding and principal component analysis (PCA) were used to build a dataset. GoogLeNet Inception-v3 (GLNI-v3) and VGG-16 models were selected to detect cotton waterlogging stress with the dataset. The results showed that the average time for a round training for GLNI-v3 was 13.337 s, with a classification accuracy of 96.95% and a loss value of 0.09431. The average time for a round training for VGG-16 was 21.470 s, with a classification accuracy of 97.00% and a loss value of 0.17912. Though these two models had similar classification accuracy and loss value, GLNI-v3 achieved a high accuracy with fewer training iterations. The durations of waterlogging stress of cotton in a short-term can be detected by HSIs of cotton leaves and CNN models are suitable for the classification of HSIs, and this method can provide support for cotton yield estimation and loss assessment after waterlogging. [ABSTRACT FROM AUTHOR]

With the characteristic of flexible and precise, unmanned aerial vehicles (UAVs) for low volume applications are increasing substantially and quickly around the globe. However, little attention has been paid to the study of wheat herbicides with UAV, especially the research on the spray volume and droplet size of the herbicide sprayed by UAVs. The objectives of this study were to compare the droplet deposition from a typical commercial UAV under four different spray volumes of 7.5 L/hm², 15.0 L/hm², 22.5 L/hm², and 30.0 L/hm² and three different volume median diameter (VMD) of 150 μm, 200 μm, and 300 μm during winter wheat weeding period. DepositScan software was used to analyze droplet deposition parameters including the percentage of spray coverage and the number of droplets in various sampling positions. The test results showed that the droplet deposition was effected by each factor and their interactions. When the spray volume was 7.5 L/hm², the effect of VMD on the percentage of spray coverage was not significant. However, these variation rules were changed to smaller droplets with greater coverage when the spray volume higher than 15.0 L/hm². In all treatments, the number of droplets increased with decreasing VMD or increasing spray volume. The maximum percentage of spray coverage and the number of droplets that were achieved under the VMD of 150 μm and the spray volume of 30.0 L/hm2 were 12.8% and 40.0 droplets/cm², respectively. The variation coefficients of the percentage of spray coverage and the number of droplets were 29.0%-73.3% and 20.2%-54.1%, respectively. The most uniform deposition was achieved under the spray volume of 15.0 L/hm² and the VMD of 150 μm. The results revealed the effect of droplet size and spray volume parameters on droplet deposition, which was useful for guiding farmers on how to use UAVs for weeding in winter wheat fields. [ABSTRACT FROM AUTHOR]

Detection and tracking of all major parts of pig body could be more productive to help to analyze pig behavior. To achieve this goal, a real-time algorithm based on You Only Look At CoefficienTs (YOLACT) was proposed. A pig body was divided into ten parts: one head, one trunk, four thighs and four shanks. And the key points of each part were calculated by the novel algorithm, which was based mainly on combination of the Zhang-Suen thinning algorithm and Gravity algorithm. The experiment results showed that these parts of pig body could be detected and tracked, and their contributions to overall pig activity could also be sought out. The detect accuracy of the algorithm in the data set could reach up to 90%, and the processing speed to 30.5 fps. Furthermore, the algorithm was robust and adaptive. [ABSTRACT FROM AUTHOR]

Nozzle flowrate and spray pressure are two of the most important factors influencing on droplet characteristics. With the aim to develop prediction models for air-induction nozzles (AINs), a series of Billericay Farm Services (BFS) AINs with different orifice diameters in combination with tap water were tested. 0.2 MPa, 0.3 MPa, 0.4 MPa, 0.5 MPa, 0.6 MPa and 0.7 MPa of spray pressures and 2 m/s, 3 m/s, 4 m/s and 5 m/s of air speeds were setup. Based on the wind tunnel tests data, prediction models with input variables of nozzle flowrate and spray pressure and output variables of Dv0.1, Dv0.5, Dv0.9, %<150 µm (proportion of spray volume contained in droplets with diameter below 150 µm), relative span (RS) and coefficient of variation (CV) of Dv0.5 were developed. The developed models were validated based on wind tunnel experimental data. Results showed that: for Dv0.1, Dv0.5, Dv0.9 and %<150 µm, R² were equal to 0.768, 0.823, 0.868 and 0.811, indicating that the predictive ability for these four parameters is strong. For RS and CV, R² were equal to 0.100 and 0.113, respectively, indicating that the predictive ability for these two parameters is poor. The models developed in the present study are helpful for facilitating the use of AIN in agricultural spray application. [ABSTRACT FROM AUTHOR]

Aerial spraying can support efficient defoliation without crop contact. With the recent introduction to unmanned aerial system (UAS) for aerial spraying in China, there is a need to determine the optimum application variables to achieve high efficacy and efficiency with low costs. The present research involved field studies across two annual cotton production seasons in North Xinjiang, China. Four factors, including volume rate (A), tank mix including spray adjuvants (B), flight altitude (C), flight speed (D) and three levels of L9 (34 ) orthogonal arrays were carried out to optimize the application parameters for three types of UASs. These included different numbers of rotors as follows: four-rotors, six-rotors and eight-rotors. Spray coverage, distribution uniformity (coefficient of variation (CV) of droplet coverage), rates of cotton defoliation and boll opening, application efficiency and cost were measured and assessed. Results showed that: (1) the rates of defoliation and boll opening by aerial cotton defoliant application could meet the requirement of cotton mechanized harvesting; (2) the optimal scenario for the three UASs was A3B2C1D3, Volume rate (A³): 48 L/hm² ; Tank mix and concentration (B2): (Tuotulong 225 + Sujie 750 + Ethephon 2250) mL/hm2, Flight altitude (C1): 1.5 m, and Flight speeds (D3) for unmanned helicopters with four-rotors, six-rotors and eight-rotors were 3.12 m/s, 2.51 m/s and 3.76 m/s, respectively. These results can provide guidance for cotton defoliant aerial spraying in China using UAS. [ABSTRACT FROM AUTHOR]