Tank-mix adjuvants improved spray performance and biological efficacy in rice insecticide application with unmanned aerial vehicle sprayer.

BACKGROUND: The use of unmanned aerial vehicles (UAVs) for the application of plant protection products (PPPs) in paddy fields is becoming increasingly prevalent worldwide. Despite its growing usage, UAV spraying for rice pest control faces practical challenges, including limited canopy penetration, uneven deposition, and significant spray drift. This study investigated the impact of two tank-mix adjuvants, Wonderful Rosin (Adjuvant-1) and Tiandun (Adjuvant-2), at six volume concentrations, on the spray liquid's physicochemical properties, spray drift, plant deposition, and the biological efficacy of rice insecticides using a quadrotor UAV sprayer. RESULTS: The physicochemical characteristics of the spray liquid influenced spray performance and biological efficacy. Incorporating Adjuvant-1 and Adjuvant-2 led to a decrease in surface tension and contact angle while increasing the viscosity of the spray solution. These alterations in surface tension and viscosity contributed to an optimized droplet size distribution, reduced spray drift, enhanced deposition uniformity and penetration, and improved control efficacy against the rice planthopper in UAV applications. The highest control efficacy was observed at a concentration of 0.5%, showing an improvement of 35.12% (Adjuvant-1) and 20.23% (Adjuvant-2) over applications without tank-mix adjuvant 7 days after treatment. CONCLUSION: The judicious selection of tank-mix adjuvants for UAV PPP applications can significantly enhance spray performance and biological efficacy in controlling rice insects. This study's findings offer valuable insights for integrating tank-mix adjuvants into UAV spraying applications. © 2024 Society of Chemical Industry.

Toward a remote sensing method based on commercial LiDAR sensors for the measurement of spray drift and potential drift reduction.

Spray drift is inevitable in chemical applications, drawing global attention because of its potential environmental pollution and the risk of exposing bystanders to pesticides. This issue has become more pronounced with a growing consensus on the need for enhanced environmental safeguards in agricultural practices. Traditionally, spray drift measurements, crucial for refining spray techniques, relied on intricate, time-consuming, and labor-intensive sampling methods utilizing passive collectors. In this study, we investigated the feasibility of using close-range remote sensing technology based on Light Detection and Ranging (LiDAR) point clouds to implement drift measurements and drift reduction classification. The results show that LiDAR-based point clouds vividly depict the spatial dispersion and movement of droplets within the vertical plane. The capability of LiDAR to accurately determine drift deposition was demonstrated, evident from the high R2 values of 0.847, 0.748 and 0.860 achieved for indoor, wind tunnel and field environments, respectively. Droplets smaller than 100 µm and with a density below 50 deposits·cm-2·s-1 posed challenges for LiDAR detection. To address these challenges, the use of multichannel LiDAR with higher wavelengths presents a potential solution, warranting further exploration. Furthermore, we found a satisfactory consistency when comparing the drift reduction classification calculated from LiDAR measurements with those obtained though passive collectors, both in indoor tests and the unmanned air-assisted sprayer (UAAS) field test. However, in environments with less dense clouds of larger droplets, a contradiction emerged between higher drift deposition and lower scanned droplet counts, potentially leading to deviations in the calculated drift potential reduction percentage (DPRP). This was exemplified in a field test using an unmanned aerial vehicle sprayer (UAVS). Our findings provide valuable insights into the monitoring and quantification of pesticide drift at close range using LiDAR technology, paving the way for more precise and efficient drift assessment methodologies.

Effect of adjuvants on physicochemical properties of lime sulfur on flower/paraffin and application on flower thinning.

Introduction: Adjuvants can effectively enhance the utilization rate of pesticides, but the application of adjuvants in plant growth regulators is rarely studied. Methods: This work explored the effects of adjuvants dioctyl sulfosuccinate sodium salt (AOT) and methyl oleate (MO) on lime sulfur (LS), especially the drop behavior on flower and paraffin surface. Results: The results showed that the addition of AOT and AOT+MO can significantly reduce the static and dynamic surface tension of LS from 72mN/m to 28mN/m and 32mN/m respectively, and increase the spreading factor from 0.18 to 1.83 and 3.10 respectively, reduce the bounce factor from 2.72 to 0.37 and 0.27 respectively. The fluorescence tracer test showed that the addition of adjuvants could promote the spreading and permeation of droplets. The field test results revealed that the flower thinning rate of adjuvant and non-adjuvant were 80.55% and 54.4% respectively, and the flower thinning effect of adding adjuvant was the same as that of artificial which the flower thinning rate was 84.77%. The quality of apples treated with adjuvants was similar to that treated with artificial, and the weight of single fruit increased by 24.08% compared with CK (spray water). Discussion: The application of tank-mixture adjuvant could reduce the dosage of LS for thinning agent application, improve apple's quality, and decrease labor cost and improve the economic benefits of fruit planting and the environmental benefits of plant growth regulators.

Spray losses study of two pesticides by UASS in integrated rice-crayfish farming system and acute toxicity evaluation on Procambarus clarkii.

Introduction: While the integrated rice-crayfish (Procambarus clarkii) farming system (IRCFS) is widely developing in China, the widespread use of Unmanned Aerial Spraying Systems (UASS) to protect rice from pests has led to potential pesticide risk for the crayfish in IRCFS. Therefore, it is crucial to examine UASS's spray deposition and drift in IRCFS. Method: In this study, we used the oligonucleotide sequence-tracking / dot-blotting (OSTDB) method to trace pesticide spraying. We collected detailed data not only on spray loss in the paddy fields, but also on spray drift in the breeding ditches caused by upwind and downwind spray areas. Additionally, pesticide residues in the breeding ditches were measured using LC-MS/MS by collecting water samples after pesticide application. Results: The data analysis indicated that the spray loss in the paddy field was significantly greater than that in the breeding ditches. The spray drift in the breeding ditches, caused by the upwind spray area, was seven times higher than that originating from the downwind spray area. Furthermore, the results also revealed that the bulk flow between the paddy fields and the breeding ditches contributed a substantial amount of pesticide residue to the water body in the breeding ditches. In addition, we investigated the acute toxicities of common insecticides using in paddy fields, including thiamethoxam (THI), chlorantraniliprole (CHI), THI·CHI-Mix and THI·CHI-WG. Discussion: The results demonstrated that the spray losses and spray drift from UASS spray applications of these pesticides in IRCFS would not cause acute toxicity or death in crayfish. These findings provide important materials for establishing pesticide application standards and guiding the field testing of droplet deposition and drift in IRCFS.

Changes of cropping structure lead diversity decline in China during 1985-2015.

China's agriculture is in the transformation and development stage to adapt to the influences of climate change, technological progress and the requirements for resources and environmental protection. Optimization of cropping structure variation in the new stage is urgent. Our study systematically described the spatiotemporal variation in crop patterns in China from 1985 to 2015 and further analyzed the changes in cropping diversity and dominant cropping structure based on a county-level agricultural database. The results showed that the planted areas of staple crops and oil crops had expanded in three major grain-producing areas. Coarse crop planting has been gradually replaced by staple and oil crops. A slight increasing trend occurred in tuber crop planting in southwestern China, and fiber crop planting had already transferred from eastern to northwestern China. Moreover, cropping diversity has decreased in northern China, especially in the Northeast China Plain and North China Plain, while a slight increase has occurred in the south. Cropping structure has been simplified in past decades and it basically formed a single cropping structure dominated by staple or oil crops. Further cropping structure adjustments should focus on resource-saving, ecofriendly, intensive and efficient industrial coordination goals, adapting to the mechanization, scale and precision developments of agricultural production. It is important to develop a multifunctional innovative farming system and technology to ensure national food security.

Field evaluation of a six-rotor unmanned agricultural aerial sprayer: effects of application parameters on spray deposition and control efficacy against rice planthopper.

BACKGROUND: Unmanned Aerial Spraying System (UASS) has emerged as an advanced, precise, and efficient tool for pesticide application in numerous nations in recent years. Despite this, there is a noticeable gap in research advocating viable, quantifiable methodologies for application parameter optimization. This investigation was primarily oriented toward identifying optimal UASS application parameters. It did so by exploring the effects of varying spray volumes and flight parameters on spray performance in a comprehensive manner, and by assessing the biological potency of aerial insecticide application against Rice Planthopper (RPH) using the optimal parameters, aided by two types of nozzles in rice field settings. RESULTS: Increased spray volume increased the spray deposition. Working height impacted the distribution of spray deposition, with a higher working height leading to superior distribution uniformity. Both spray volume and working height were observed to influence spray deposition and its percentage in tandem. Upon factor analysis, the optimal parameters determined for rice at the heading stage were an application volume of 15.0 L·ha-1 , a working height of 2.0 m, and a driving speed of 5.0 m·s-1 . Under these parameters, the air-induction twin flat fan nozzle IDKT120-015 demonstrated approximately 5% higher spray deposition than the flat fan nozzle SX11001VS, albeit with inferior distribution uniformity. Both nozzle types achieved over 93.0% control efficacy against RPH using triflumezopyrim, persisting for up to 40 days post-treatment. CONCLUSION: This study furnishes invaluable insights and data for controlling rice planthopper via UASS pesticide application, contributing to the progress of modern intensive and sustainable agriculture. © 2023 Society of Chemical Industry.

Nano-based smart formulations: A potential solution to the hazardous effects of pesticide on the environment.

Inefficient usage, overdose, and post-application losses of conventional pesticides have resulted in severe ecological and environmental issues, such as pesticide resistance, environmental contamination, and soil degradation. Advances in nano-based smart formulations are promising novel methods to decrease the hazardous impacts of pesticide on the environment. In light of the lack of a systematic and critical summary of these aspects, this work has been structured to critically assess the roles and specific mechanisms of smart nanoformulations (NFs) in mitigating the adverse impacts of pesticide on the environment, along with an evaluation of their final environmental fate, safety, and application prospects. Our study provides a novel perspective for a better understanding of the potential functions of smart NFs in reducing environmental pollution. Additionally, this study offers meaningful information for the safe and effective use of these nanoproducts in field applications in the near future.

The pattern of abandoned cropland and its productivity potential in China: A four-years continuous study.

The increased requirement of food production with the rising population challenges limited cultivation land in China. The abandoned cropland has high potential in grain production to ensure China's food security. However, the spatial distributions of abandoned cropland in China are understudied and therefore it is difficult to estimate its potential grain production. Our study proposed a new definition of abandoned cropland considering unique multiple cropping systems in China, and estimate the abandoned cropland distribution and grain productivity potential by using Landsat-8 and GF-1 images under deep learning technology. The area of abandoned cropland in three main grain-producing regions was approximately 1.53 million hectares during 2014-2017. The estimated images agreed with the field survey and the national agricultural statistical data with the accuracy larger than 87 %. The spatial distribution of abandoned cropland in China was scattered and a high abandonment rate observed in the Middle-lower Yangtze River Plain. Moreover, the uncultivated cropland accounted for approximately 50 % of the total area of abandoned cropland. The maximum production potential of abandoned cropland could reach 8.5 million tons, including 2.7, 2.5 and 3.3 million tons of maize, wheat and rice, respectively. The exploitation of abandoned cropland is also beneficial for additional soybean production in China. National-scale estimation of abandoned cropland in China is crucial for land use policy making and cropland protection, as well the implementation of national food security strategy.

Field evaluation of spray drift and nontargeted soybean injury from unmanned aerial spraying system herbicide application under acceptable operation conditions.

BACKGROUND: Droplets of plant production products sprayed from unmanned aerial spraying system (UASS) applications are prone to drift, threatening nontarget crops, humans, and environment. There are few studies that have investigated plant bioassay of UASS spray drift, and even fewer when it comes to herbicide application. This work reports a combined field-scale evaluation of spray drift and plant bioassay for a rice herbicide florpyrauxifen-benzyl application using a six-rotor motor UASS under acceptable operating conditions. An artificial rice canopy was built to simulate a practical field application scenario and the soybean was applied to assess the nontargeted crop injury. The effects of nozzle type (droplet size), flight height, and adjuvant on spray deposition, sedimenting drift, airborne drift, and soybean injury were studied to explore the feasibility of UASS herbicide application. RESULTS: Under an average wind speed of 1.2-1.5 m s-1 , reduced flight height, increased droplet size, and adding nonionic surfactant resulted in greater deposition, lower drift, and less injury to soybean. Increasing droplet size by changing the nozzle was more effective compared with adding adjuvant and reducing the flight height, which offers greater flexibility and can accomplish better spray performance. The correlations between sedimenting drift and soybean injury percentage were highly significant (P < 0.01, r > 0.96). The calculated buffer distances of 7.7-18.9 m were to varying degrees less than the soybean safety distances of 10.0-20.0 m. CONCLUSION: The results of this study provide a reference basis for determining optimum working parameters and establishing buffer zones for the rice herbicide application of UASS. © 2022 Society of Chemical Industry.

Comprehensive assessment of intelligent unmanned vehicle techniques in pesticide application: A case study in pear orchard.

The intelligent pesticide application techniques in orchards have grown rapidly worldwide due to the decrease in agricultural populations and the increase in labor costs. However, whether and how intelligent pesticide application techniques are better than conventional pesticide application remains unclear. Here, we evaluated the performance of the unmanned aircraft vehicle (UAV) and unmanned ground vehicle (UGV) on pesticide application, ecological environment protection, and human's health protection compared to conventional manual methods. We quantified characteristics from the aspects of working effectiveness, efficiency, environmental pollution, water saving and carbon dioxide reduction. The results showed that the UAV application has the advantages of a higher working efficiency and less environmental pollution and natural resource consumption compared to the UGV and conventional manual methods despite of its worse spray performance The UGV application techniques could improve spray performance at the cost of high environmental pollution. The conventional spray gun technique was unfriendly to environmental and resource protection although it showed a better spray performance. Thus, the balance of improving spray performance and controlling environmental pollution is the key to improve the performance of UAV and UGV technology in the future. The study could be useful in the development of intelligent pesticide application techniques and provide scientific support for the transition of intelligent management in orchards.

Development of multifunctional unmanned aerial vehicles versus ground seeding and outplanting: What is more effective for improving the growth and quality of rice culture?

The agronomic processes are complex in rice production. The mechanization efficiency is low in seeding, fertilization, and pesticide application, which is labor-intensive and time-consuming. Currently, many kinds of research focus on the single operation of UAVs on rice, but there is a paucity of comprehensive applications for the whole process of seeding, fertilization, and pesticide application. Based on the previous research synthetically, a multifunctional unmanned aerial vehicle (mUAV) was designed for rice planting management based on the intelligent operation platform, which realized three functions of seeding, fertilizer spreading, and pesticide application on the same flight platform. Computational fluid dynamics (CFD) simulations were used for machine design. Field trials were used to measure operating parameters. Finally, a comparative experimental analysis of the whole process was conducted by comparing the cultivation patterns of mUAV seeding (T1) with mechanical rice direct seeder (T2), and mechanical rice transplanter (T3). The comprehensive benefit of different rice management processes was evaluated. The results showed that the downwash wind field of the mUAV fluctuated widely from 0 to 1.5 m, with the spreading height of 2.5 m, and the pesticide application height of 3 m, which meet the operational requirements. There was no significant difference in yield between T1, T2, and T3 test areas, while the differences in operational efficiency and input labor costs were large. In the sowing stage, T1 had obvious advantages since the working efficiency was 2.2 times higher than T2, and the labor cost was reduced by 68.5%. The advantages were more obvious compared to T3, the working efficiency was 4 times higher than in T3, and the labor cost was reduced by 82.5%. During the pesticide application, T1 still had an advantage, but it was not a significant increase in advantage relative to the seeding stage, in which operating efficiency increased by 1.3 times and labor costs were reduced by 25%. However, the fertilization of T1 was not advantageous due to load and other limitations. Compared to T2 and T3, operational efficiency was reduced by 80% and labor costs increased by 14.3%. It is hoped that this research will provide new equipment for rice cultivation patterns in different environments, while improving rice mechanization, reducing labor inputs, and lowering costs.

Design and development of orchard autonomous navigation spray system.

Driven by the demand for efficient plant protection in orchards, the autonomous navigation system for orchards is hereby designed and developed in this study. According to the three modules of unmanned system "perception-decision-control," the environment perception and map construction strategy based on 3D lidar is constructed for the complex environment in orchards. At the same time, millimeter-wave radar is further selected for multi-source information fusion for the perception of obstacles. The extraction of orchard navigation lines is achieved by formulating a four-step extraction strategy according to the obtained lidar data. Finally, aiming at the control problem of plant protection machine, the ADRC control strategy is adopted to enhance the noise immunity of the system. Different working conditions are designed in the experimental section for testing the obstacle avoidance performance and navigation accuracy of the autonomous navigation sprayer. The experimental results show that the unmanned vehicle can identify the obstacle quickly and make an emergency stop and find a rather narrow feasible area when a moving person or a different thin column is used as an obstacle. Many experiments have shown a safe distance for obstacle avoidance about 0.5 m, which meets the obstacle avoidance requirements. In the navigation accuracy experiment, the average navigation error in both experiments is within 15 cm, satisfying the requirements for orchard spray operation. A set of spray test experiments are designed in the final experimental part to further verify the feasibility of the system developed by the institute, and the coverage rate of the leaves of the canopy is about 50%.

Rh(I)-Catalyzed Coupling of Azides with Boronic Acids Under Neutral Conditions.

Because of the importance of polyfunctional amines, C-N bond formation is important in synthetic organic chemistry. Here we present a neutral amination reaction using azides as the nitrogen source and arylboronic acids with a rhodium(I) catalyst to afford alkyl-aryl and aryl-aryl secondary amines. Natural products and pharmaceutical derivatives were applied, and gram-scale reactions were performed, which demonstrated the utility. Mechanistic experiments and DFT calculations suggested that the reaction involves a metal-nitrene intermediate.

Spray performance evaluation of a six-rotor unmanned aerial vehicle sprayer for pesticide application using an orchard operation mode in apple orchards.

BACKGROUND: In the last decade, unmanned aerial vehicle (UAV) sprayers have been growing rapidly worldwide as a new method for pesticide application, especially in Asian countries. More and more manufacturers and service providers are currently aiming at UAV spraying operation for fruit trees with higher economic value. We evaluated the spray performance of an electric six-rotor UAV sprayer using an orchard operation mode (different application volumes and flight patterns) in a hilly apple orchard with small and sparse trees (SS) and a plain orchard with tall-spindle trees (TS). RESULTS: Application volume (APV) had a significant influence on the spray coverage parameters in both orchards, while flight pattern, intra-row, inter-row and verti-row, had a relatively limited influence at 60 0 and 85 7 L/ha. The UAV's downwash airflow produced a good spray penetration in the isolated SS trees, but not for the conjoined TS trees. It is better to fly along and above rows at 63.5 L/ha or higher for SS trees. The excessively low underside coverage is the main drawback of UAV orchard pesticide application and the underside droplet size was generally less than 200 µm. CONCLUSION: Spray performance is closely related to tree shape, planting pattern, UAV payload, application volume, spray droplet size and downwash airflow field. The results provide data support for the best operational practice development and the decision model for the application volume of UAV sprayer orchard operations. The underside spray performance requires further improvement by several effective measures. © 2022 Society of Chemical Industry.

An adaptive composite disturbance rejection for attitude control of the agricultural quadrotor UAV.

Aiming at the problem that agricultural quadrotor UAV is easily disturbed in ultra-low altitude phenotype remote sensing and precision hovering of spraying, an adaptive composite anti-disturbance attitude controller is proposed for ground effect and propeller failure disturbances rejection. The adaptive composite disturbance rejection control (ACDRC) is composed of active disturbance rejection control (ADRC) and disturbance observer (DO) based on nominal inverse model, which is used to estimate wind disturbance, payload disturbance and propeller failure disturbance in real time. For the bandwidth tuning of the extended state observer (ESO), an online tuning method based on iterative learning control (ILC) is proposed to realize the adaptive extended state observer (ESO). And the stability of the composite anti-disturbance controller is analyzed. In the experiments, the wind disturbance experiments under the side-down flow and the horizontal flow, the failure experiments under the single propeller failure and twin propeller failure, and the composite disturbances experiments under the simultaneous action of the wind disturbance, propeller failure and payload disturbance are carried out. The experimental results show that under wind disturbance, the anti-disturbance performance of ACDRC is increased by 82.5%; under the disturbance of propeller fault, the anti-disturbance performance of ACDRC is increased by 60%; under the composite disturbance, the anti-disturbance performance of ACDRC is increased by 50%. Finally, the effectiveness of ACDRC is further verified in vegetable and cotton fields.

Diagnosis of Typical Apple Diseases: A Deep Learning Method Based on Multi-Scale Dense Classification Network.

Disease has always been one of the main reasons for the decline of apple quality and yield, which directly harms the development of agricultural economy. Therefore, precise diagnosis of apple diseases and correct decision making are important measures to reduce agricultural losses and promote economic growth. In this paper, a novel Multi-scale Dense classification network is adopted to realize the diagnosis of 11 types of images, including healthy and diseased apple fruits and leaves. The diagnosis of different kinds of diseases and the same disease with different grades was accomplished. First of all, to solve the problem of insufficient images of anthracnose and ring rot, Cycle-GAN algorithm was applied to achieve dataset expansion on the basis of traditional image augmentation methods. Cycle-GAN learned the image characteristics of healthy apples and diseased apples to generate anthracnose and ring rot lesions on the surface of healthy apple fruits. The diseased apple images generated by Cycle-GAN were added to the training set, which improved the diagnosis performance compared with other traditional image augmentation methods. Subsequently, DenseNet and Multi-scale connection were adopted to establish two kinds of models, Multi-scale Dense Inception-V4 and Multi-scale Dense Inception-Resnet-V2, which facilitated the reuse of image features of the bottom layers in the classification neural networks. Both models accomplished the diagnosis of 11 different types of images. The classification accuracy was 94.31 and 94.74%, respectively, which exceeded DenseNet-121 network and reached the state-of-the-art level.

Assessment of spray deposition, drift and mass balance from unmanned aerial vehicle sprayer using an artificial vineyard.

Under the rapid development of unmanned aerial vehicle (UAV) plant protection products (PPP) application in Asian countries, the drift risk of UAV sprayer operation in orchard or vineyard is fairly high because of the much finer droplets generated and the higher height than ground sprayers, increasing threats to non-targeted crop, human and environment. However, there is few of comprehensive experimental study on the effects of UAV type and nozzle type on spray deposition and drift from UAV sprayer. The objectives of this study were to compare the spray performance of three different typical commercial UAV types (helicopter, 6-rotor and 8-rotor) with two nozzles types (hollow cone nozzle, HCN and air-injector flat fan nozzle, AIN) in vineyard. An artificial vineyard and three vertical collection frames, designed and built by ourselves, were applied for collecting droplets together with PVC collectors, petri dishes and rotary samples. The characteristics of deposition, drift and mass balance of UAV aerial spraying in vineyard were analyzed. As a result, under the crosswind speed of 3.11-3.79 m/s, AIN promoted spray deposition and uniformity and reduced drift significantly compared to HCN for all tested UAVs, improving of the utilization of PPP. The fitted regression functions of the sedimenting and airborne drift were obtained, respectively, and the drift percentage reduction values of AIN compared to HCN determined based on those functions varied from 81% to 95%. With HCN, 49.3%-73.4% of measured droplets drifted into non-targeted area and the highest proportion of drift loss was found for the airborne spray drift. According to the principle of more deposition and less drift, the spray performance of the three UAVs can be ranked in an order of 6-rotor, 8-rotor and helicopter, and two main reasons causing the difference in spray performance were the vortex airflow and the nozzle arrangement.

Butyrylcholinesterase nanodepots with enhanced prophylactic and therapeutic performance for acute organophosphorus poisoning management.

Acute organophosphorus pesticide poisoning (AOPP) is a worldwide health concern that has threatened human lives for decades, which attacks acetylcholinesterase (AChE) and causes nervous system disorders. Classical treatment options are associated with short in vivo half-life and side effects. As a potential alternative, delivery of mammalian-derived butyrylcholinesterase (BChE) offers a cost-effective way to block organophosphorus attack on acetylcholinesterase, a key enzyme in the neurotransmitter cycle. Yet the use of exotic BChE as a prophylactic or therapeutic agent is compromised by short plasma residence, immune response and unfavorable biodistribution. To overcome these obstacles, BChE nanodepots (nBChE) composed of a BChE core/polymorpholine shell structure were prepared via in situ polymerization, which showed enhanced stability, prolonged plasma circulation, attenuated antigenicity and reduced accumulation in non-targeted tissues. In vivo administration of nBChE pre- or post-organophosphorus exposure in a BALB/C mouse model resulted in potent prophylactic and therapeutic efficiency. To our knowledge, this is the first systematic delivery of non-human BChE to tackle AOPP. In addition, this work also opens up a new avenue for real applications in both research and clinical settings to cope with acute intoxication-related diseases.

Weed Density Extraction Based on Few-Shot Learning Through UAV Remote Sensing RGB and Multispectral Images in Ecological Irrigation Area.

With the development of ecological irrigation area, a higher level of detection and control categories for weeds are currently required. In this article, an improved transfer neural network based on bionic optimization to detect weed density and crop growth is proposed, which used the pre-trained AlexNet network for transfer learning. Because the learning rate of the new addition layer is difficult to tune to the best, the weight and bias learning rate of the newly added fully connected layer is set with particle swarm optimization (PSO) and bat algorithm (BA) to find the optimal combination on the small data set. Data are transported to the convolutional neural network (CNN) by collecting red-green-blue (RGB) and 5-band multispectral images of 3 kinds of weeds and 3 kinds of crops as data sets, through cutting, rotating, and other operations. Finally, 6 kinds of classifications are implemented. At the same time, a self-constructed CNN based on model-agnostic meta-learning (MAML) is proposed in order to realize the learning of neural networks with small sample and high efficiency, and its accuracy is verified in the test set. The neural networks optimized by two bionic optimization algorithms are compared with the self-constructed CNN based on MAML and histogram of oriented gradient + support vector machine (HOG + SVM). The experimental results show that the combination of learning rate through BA is the best, and its accuracy can reach 99.39% for RGB images, 99.53% for multispectral images, and 96.02% for a 6-shot small sample. The purpose of the classification proposed in this article is to calculate the growth of various plants (including weeds and crops) in the farmland. And various plant densities can be accurately calculated through the plant density calculation formula and algorithm proposed in this article, which provides a basis for the application of variable herbicides by experimenting in different farmlands. Finally, an excellent cycle of ecological irrigation district can be promoted.