Agricultural Environment and Intelligent Plant Protection Equipment

With the aim of solving the problems of high labor intensity, low operational efficiency, and poor deposition distribution uniformity in the mango canopy associated with traditional plant protection devices, an air-ground co-operation stereoscopic plant protection system consisting of an orchard caterpillar mist sprayer and a six-rotor plant protection UAV was designed to jointly undertake plant protection operations in mango orchards. We tested the spraying performance of the system on mango trees, compared with the single-machine operation, the air–ground co-operation system could significantly increase the droplet coverage on the upperside of mango leaves in each part of the canopy (a 14.7% increase for the mist sprayer and 12.9% for the UAV). This increased the active component deposition distribution uniformity in the mango canopy but could not significantly improve the deposition and coverage of droplets on the underside of leaves compared with the mist sprayer and plant protection UAV. Due to the characteristics of the mango canopy such as large leaf length and thickness and complex leaf inclination distribution, this led to poor deposition distribution uniformity of the two spray units, and the overall CV was over 150%. The pesticide active ingredients were almost uniformly distributed in the vertical direction when the application ratios (ground implements/plant protection drones) were 8/2 and 7/3, offering a promising protocol for reduced pesticide application in mango orchards. This study presents promising data that support the innovative integration of drones into crop protection programs for large canopy crops (e.g., mango) and provides guidance for the ACSPPS system in reduction and precision application research. Full article

In order to clarify the temporal and spatial changes in the internal photothermal environment in an open-field agrivoltaic system (OAVS), this paper took the OAVS in eastern China as the research object and divided the internal area into the southern area, middle area and northern area, according to the spatial structure. Further, a photothermal environment test was conducted in the above three areas in the summer and winter. The results showed that the summer average daylight rate (R_m-avg) in the middle area was 66.6%, while the R_m-avg in the other two areas was about 20%, with no significant difference. In the winter, the light environment in the southern area was slightly better, and the R_m-avg in the above three areas was 26.4%, 24.7% and 19.7%, respectively. On the whole, the relationship between the thermal environmental factors and the solar radiation intensity was consistent. Further, a 3D model of an OAVS was established using Autodesk Ecotect Analysis 2011, and the internal light environment was simulated. Compared with the measured values, the relative error was less than 10%, which verified the reliability of the OAVS model. Then, the model was used to reveal the temporal and spatial changes in the light environment of the OAVS. The simulation results showed that the daylighting rate in the summer from the ground to the height of the fig canopy inside the system was 20.7% to 61.5%. In the winter, the daylighting rate from the ground to the height of the fig canopy inside the system was 17.7% to 36.4%. The effectiveness of the OAVS in reducing the level of solar radiation intensity depended on the time of day and the angle of the sun. At the spatial scale, due to the strong consumption of light by photovoltaic panels, there was a strong horizontal and vertical light environment gradient inside the system. In conclusion, the photothermal environment research of an OAVS based on Autodesk Ecotect Analysis 2011 can not only provide a basis for agricultural production and structural design such as span, height and the laying density of PV panels, but also expand its application to regions with different latitudes and longitudes and specific climates. Full article

In the past decade, an unmanned aerial spraying system (UASS) was applied more and more widely for low-volume aerial pesticides spraying operations in China. However, UASS have a higher drift risk due to more fine droplets sprayed with a higher working height and a faster driving speed than ground sprayers. Study on UASS spray drift is a new hot spot within the field of pesticide application technology. The field test bench was originally designed and applied for the measurement of the spray drift potential of ground sprayers. No methodology using the test bench for UASS drift evaluation was reported. Based on our previous study, field drift measurements of an eight-rotor UASS were conducted using three techniques (test bench, ground petri dish, and airborne collection frame) in this study, and the effects of meteorological parameters and nozzle types were investigated, to explore the applicability and the feasibility of the test bench used in UASS field drift evaluation. The test bench is proven promising for direct drift determination of UASS and the described methodology enabled classification of different UASS configurations. Higher wind speeds and finer droplets produced higher drift values. The faster the wind speed and the lower the humidity, the more the spray drift. The test bench can reduce the site requirements and improve the efficiency of the field drift test. Full article

Off-target drift of crop protection materials from aerial spraying can be detrimental to sensitive crops, beneficial insects, and the environment. So, it is very important to accurately characterize weather effects for accurate recommendations on drift mitigation. Wind is the single-most important weather factor influencing localized off-target drift of crop protection materials. In drift sampling experiments, it is difficult to accurately characterize wind speed and direction at a drift sampling location, owing to the natural variability of spray movement on the way to the sampling target. Although it is difficult or impossible to exactly track wind movement to a target, much information can be gained by altering the way wind speed and tracking is characterized from field experiments and analyzed using statistical models of spray drift. In this study two methods of characterizing weather were compared to see how they affect results from a statistical model of downwind spray drift using field data. Use of a method that implemented weather averages over the length of a spray run resulted in model-based estimates for spray tracer concentration that compared well with field data averages. Model results using this method showed only a slight sensitivity to changes in wind speed, and this difference was more pronounced further downwind. The degree of this effect was consistent with field results. Another method that used single weather values obtained at the beginning of each run resulted in an unexpected inverse relationship of residue concentration with respect to increases in wind speed by sensitivity analysis and would thus not be recommended for use in a statistical model of downwind spray drift. This study could provide a guideline for general agricultural aviation analysis and unmanned aerial vehicle spray application studies. Full article

Insecticide applications are typically being carried out with traditional manual spraying equipment in redgram, which leads to inadequate control of insects due to higher crop height. The modern deployment of tractor-drawn spray machines causes serious damage to the crop. In this connection, unmanned aerial vehicle (UAV) spray technology has great potential for precise insecticide application in redgram crops. One of the important machine parameters influencing droplet deposition and drift characteristics in UAV sprayers is downwash airflow generated by a multi-rotor propeller. A field experiment was carried out at the redgram research field (N11.01, E76.92), Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, during 2021–2022 to study the spray drift and deposition characteristics of an autonomous UAV sprayer. The Imidacloprid (a.i. 17.8SL) insecticide mixed with water in a ratio of 1 mL per liter was sprayed with a UAV sprayer. Water-sensitive paper samples were kept at upper, middle, and bottom positions on the leaves, and data were analyzed for the spray droplet size, deposition rate, droplet density, and area coverage both in target and non-target areas using Spray Deposit Scanner software. UAV spray droplet deposition rate (2.93 ± 0.17, 2.01 ± 0.08, and 2.21 ± 0.162.38 μL cm⁻²), droplet density (47 ± 4.04, 53 ± 3.61, and 52 ± 8.74 droplets cm⁻²), and area coverage (15.72 ± 0.39, 16.60 ± 0.71, and 14.99 ± 0.39%) were highest in the upper layer as compared to the middle layer (droplet deposition rate: 1.21 ± 0.08, 1.07 ± 0.03, and 0.77 ± 0.02 μL cm⁻²; droplet density: 42 ± 2.52, 43 ± 8.50, and 38 ± 2.52 droplets cm⁻²; area coverage: 10.95 ± 0.81, 11.22 ± 0.56, and 8.57 ± 0.44%) and bottom layer (droplet deposition rate: 0.41 ± 0.06, 0.35 ± 0.03, and 0.33 ± 0.03 μL cm⁻²; droplet density: 22 ± 4.36, 17 ± 3.51, and 19 ± 4.51 droplets cm⁻²; area coverage: 2.78 ± 0.29, 2.95 ± 0.45, and 2.46 ± 0.20%, respectively). In the spray drift test, there was a higher droplet deposition rate (1.63 ± 0.09, 1.93 ± 0.05, and 1.82 ± 0.06 μL cm⁻²), area coverage (14.40 ± 0.07, 17.54 ± 0.36, and 16.42 ± 0.30%), and droplet density (46 ± 3.61, 54 ± 2.08, and 45 ± 3.21 No’s cm⁻²) in the target area as compared to the non-target area (droplet deposition rate: 0.88 ± 0.02, 0.46 ± 0.03, 0.22 ± 0.05, and 0.00 μL cm⁻²; droplet density: 23 ± 1.53, 11 ± 2.08, 6 ± 1.53, and 0.00 droplets cm⁻²; area coverage: 7.58 ± 0.34, 4.41 ± 0.19, 2.16 ± 0.05, and 0.00%, respectively), which may have been due to the downwash airflow produced by the multi-rotor propeller of the UAV sprayer. Finally, the UAV-based spraying technology results showed that the downwash air produced by the six-rotor propeller improved the penetrability of insecticide to crop leaves and led to a higher droplet deposition rate, droplet density, area coverage, and droplet penetrability on the upper layer, middle layer, and bottom layer of the plants. Full article

To solve the problem encountered when the spraying robot has run out of medicine even though the spraying task on the field is not complete, we developed a spraying–dosing robot group and proposed a collaborative navigation system based on an orchard map. Firstly, we constructed a 3D orchard point cloud map and set up navigation path points on the projected map. Secondly, we developed a master–slave command-based cooperative navigation strategy, where the spraying robot was the master and the dosing robot was the slave. Finally, the spraying robot and the dosing robot completed the cooperative navigation on the constructed map by using the pure pursuit algorithm and D-A control algorithm, respectively. To validate the cooperative navigation system, we conducted field tests on the separate communication and navigation control. The results of communication experiments demonstrated that the packet loss rate was less than 5%, which satisfied communication requirements. The experimental results of the navigation control demonstrated that the maximum value of the absolute lateral error is 24.9 cm for the spraying robot and 29.7 cm for the dosing robot. The collaborative navigation system proposed in this research can meet the automatic navigation requirements of the spraying–dosing robot group for collaborative tasks in traditional orchards. Full article

The precise path-tracking control of tractors and trailers is the key to realizing agricultural automation. In order to improve the path-tracking control accuracy and driving stability of orchard traction spraying robots, this study proposed a navigation path-tracking control algorithm based on Double Deep Q-Network (Double DQN). Drawing on the driver’s driving experience and referring to the principle of radar scanning and the principle of image recognition, a virtual radar model was constructed to generate a virtual radar map. The virtual radar map was used to describe the position relationship between the traction spraying robot and the planned path. Combined with the deep reinforcement learning method, all possible robot driving actions under the current virtual radar map were scored, and the best driving action was selected as the output of the network. In this study, a path-tracking algorithm was self-developed with a deep Q-network trained by driving the traction spraying robot in a simulated virtual environment. The algorithm was tested in both simulations and in a field to follow a typical ‘U’-shaped path. The simulation results showed that the proposed algorithm was able to achieve accurate path-tracking control of the spraying trailer. The field tests showed that when the vehicle speed was 0.36 m/s and 0.75 m/s, the maximum lateral deviation of the algorithm was 0.233 m and 0.266 m, the average lateral deviation was 0.071 m and 0.076 m, and the standard deviation was 0.051 m and 0.057 m, respectively. Compared with the algorithm based on the virtual radar model, the maximum lateral deviation was reduced by 56.37% and 51.54%, the average lateral deviation was reduced by 7.8% and 5.0%, and the standard deviation was reduced by 20.31% and 8.1%, respectively. The results showed that the Double-DQN-based navigation path-tracking control algorithm for the traction spraying robot in the orchard had higher path-tracking accuracy and driving stability, which could meet the actual operational requirements of traditional orchards. Full article

Soil erosion is a major form of land degradation, especially in agroecosystems, which has been effectively controlled by vegetation restoration. However, the interactive role of erosion and cropland revegetation on soil enzyme activities and microbial nutrient limitations is less understood. To address this issue, we examined carbon (C), nitrogen (N), and phosphorus (P) in bulk soils and microbial biomass, enzyme activities, and microbial nutrient limitations in the 0–200 cm soils in eroded and deposited landscapes occupied by cropland, revegetated forest, and grassland. The results showed that the activities of C-, N-, and P-acquiring enzymes were larger in the deposited landscape than in the eroded landscape for 0–20 cm soils in forest and grassland but not in cropland. Microbial metabolism was co-limited by N and P, and the threshold element ratio (TER_L) indicated that P was the most limiting factor. Microbial N limitation was lower in the deposited than the eroded zone, especially in surface soils in revegetated forest and grassland. The TER_L value was larger at the deposited than at the eroded zone, and a greater difference was found in the surface soils of forest and grassland. Microbial nutrient limitations were mostly explained by C/P and N/P. Conclusively, the deposited areas were characterized by ameliorated enzyme activities, decreased microbial N limitation but relatively strengthened microbial P limitation compared to the eroded area, and such variations existed in the revegetated forest and grassland but not in the cropland, which thus contributes to a better understanding of C and nutrient cycling for agroecosystems and revegetation ecosystems in eroded environments. Full article

This study uses UAV multi-spectral remote sensing images to carry out ground object classification research in complex wheat field scenes with diverse varieties. Compared with satellite remote sensing, the high spatial resolution remote sensing images obtained by UAVs at low altitudes are rich in detailed information. In addition, different varieties of wheat have different traits, which makes it easy to misclassify categories in the process of semantic segmentation, which reduces the classification accuracy and affects the classification effect of ground object. In order to effectively improve the classification accuracy of ground object in complex wheat field scenes, two Multi-Scale U-Nets based on multi-scale feature fusion are proposed. Multi-Scale U-Net1 is a network model that adds a multi-scale feature fusion block in the copy process between U-Net encoding and decoding. Multi-Scale U-Net2 is a network model that adds a multi-scale feature fusion block before U-Net inputs an image. Firstly, the wheat field planting area of Institute of Water-saving Agriculture in Arid Areas of China (IWSA), Northwest A&F University was selected as the research area. The research area was planted with a variety of wheat with various types of traits, and some traits were quite different from one another. Then, multi-spectral remote sensing images of different high spatial resolutions in the study area were obtained by UAV and transformed into a data set for training, validation, and testing of network models. The research results showed that the overall accuracy (OA) of the two Multi-Scale U-Nets reached 94.97% and 95.26%, respectively. Compared with U-Net, they can complete the classification of ground object in complex wheat field scenes with higher accuracy. In addition, it was also found that within the effective range, with the reduction of the spatial resolution of remote sensing images, the classification of ground object is better. Full article

The effective quantification of deposition rate is of vital importance in optimizing the application performance and the utilization of pesticides; meanwhile, the canopies of fruit tree orchards are large, with dense branches and leaves shading each other, making it difficult to quantify spraying efficiency. Therefore, it is imperative to develop a facile methodology for assessing the performance of different spraying techniques in terms of distribution and utilization rate in orchards. To evaluate spraying efficacy in orchards, a canopy segmentation method was developed in to be able to determine the spray deposition rate. The distribution and deposition rate of spray liquid applied using three kinds of orchard sprayer were measured in a pear orchard and a peach orchard. The test results showed that the trailer sprayer had the highest deposition rates, with values of 31.54% and 56.92% on peach and pear trees, respectively. The deposition rates of the mounted sprayer in the peach and pear canopies were 21.75% and 40.61%, and the rates of the hand-held sprayer were 25.19% and 29.97%, respectively. The spray gun had the best droplet distribution uniformity, with CVs of the spray in the peach and pear canopies of 20.54% and 25.06%, respectively. The CVs in the peach and pear canopies were 35.98% and 26.54% for the trailer sprayer, and the CVs of the mounted sprayer were 92.52% and 94.90%, respectively. The canopy segmentation method could effectively be used to calculate the deposition rate and drioplet distribution in orchard application, while a great deal of time was consumed by counting the number of leaves in the different areas of the fruit tree canopies. Therefore, research on the density of branches and leaves in fruit tree canopies should be carried out in order to improve the efficiency of fruit tree canopy information extraction. Full article

Pipeline spraying can be adopted for greatly improving spraying efficiency in hillside orchard spraying operations. However, residual phytosanitary product still remains in the pipeline after the completion of pipeline spraying operations. Currently, residual phytosanitary liquid is handled according to the following general method: pipeline flushing with fresh water. The method can easily lead to pesticide waste and environment pollution. On-line pesticide mixing technology can be adopted for reducing pesticide waste and environmental pollution. However, on-line pesticide mixing technology is not applied in pipeline spraying operations. Therefore, the mixing principle of jet-mixing apparatus is adopted as a reference in the paper for designing the basic structure of on-line pesticide mixing apparatus based on pipeline spraying. The structure is mainly composed of a constricted tube, suction chamber, Venturi, and diffusion tube. An analysis method based on the CFD orthogonal experiment is adopted for studying the influence of the changes of four key structure parameters on on-line pesticide mixing apparatus, pesticide dissolution performance, and pesticide mixing performance; the four parameters include constricted tube falloff angle, diffusion tube divergence angle, Venturi diameter, and Venturi length. Since there may be interaction among them, three experiment evaluation indexes of lifting height, turbulent kinetic energy, and pressure recovery distance are set for judgment. The change of three evaluation indexes with change of constricted tube falloff angle, diffusion tube divergence angle, Venturi diameter, and Venturi length, respectively, is revealed through single-index variance analysis; the three indexes include lifting height, turbulent kinetic energy, and pressure recovery distance. The primary and secondary sequences of respective influences of all structure parameters and their interaction on all evaluation indexes are obtained. Analysis results of all evaluation indexes are comprehensively considered in order to finally discover the comprehensive optimal pesticide mixing apparatus structure parameters, namely: constricted tube falloff angle is 22°, diffusion tube divergence angle is 9°, Venturi diameter is 2 mm, Venturi length is 6 mm, and pesticide mixing apparatus structure parameters are optimized. Theoretical reference is provided in the paper for on-line pesticide mixing apparatus prototype production on the basis of pipeline spraying. Full article

Facing the need of modern agriculture to accurately grasp the information of farmland diseases and pests, this paper proposes an intelligent plant protection system. The system is composed of a wireless lens, temperature and humidity sensor, intelligent information terminal, and probe rod to realize the collection of plant images and meteorological information. At the same time, a software based on the mobile terminal and the computer terminal was developed. The plant images and meteorological data are transmitted to the server through Wi-Fi transmission. Combined with the expert knowledge model, a solution is generated, and the user can identify the current diseases and pests and obtain solutions at any time. The system can remotely and automatically monitor and warn of mainstream diseases and pests of field crops such as rice and wheat and provide support for fine plant protection management. Full article

Efficient utilization is a pre-requisite for pesticide reduction, and appropriate airflow distribution pattern plays a key role in enhancing the effectiveness of pesticide application by air-assisted orchard sprayers, yet the mechanism of this is unclear. In order to clarify the specific effects of airflow velocity and direction on spraying efficacy, a series of spray tests on pear and cherry and airflow distribution tests in open areas were conducted by a multi-unit air-assisted sprayer on ten different fan settings. Several deposit indicators were analyzed and contrasted with the air distribution. The results showed that an increase in airflow velocity inside the canopy improved the abaxial side deposit coverage of both pear (from 3.33% to 11.80% in the Top canopy and from 6.26% to 11.00% in the Upper canopy) and cherry leaves (from 3.61% to 10.87% in the Top canopy, from 1.36% to 9.04% in the Middle canopy, and from 3.40% to 9.04% in the Bottom canopy), but had no significant effect on the spray penetration. The correlation between deposit indicators and airflow velocities/directions was evaluated, and the results indicated that the enhanced airflow velocities, both in the forward and horizontal direction, improved the abaxial side deposit coverage (CAB) on the outside of pear canopy (p < 0.001), but for cherry, none of the airflow indicators had a significant impact on the CAB independently. On the other hand, the increased airflow direction angle in the cross-row plane for pear, as well as the increased airflow velocities in forward and vertical direction for cherry, both showed negative effects on the adaxial side deposit coverage (p < 0.01). The findings in this study might be helpful to improve the performance of pesticide application in orchards, especially for abaxial side deposition, and could provide a reference for the further investigations about the effect of airflow on spray canopy deposition. Full article

Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases. Full article