Research on a Robust Backstepping Attitude Controller for Multi-rotor Plant Protection UAV

The plant protection UAV is an important application equipment for precision spraying technology. However, in the process of spraying, the change of its own load will lead to the decline of control performance and disturbance rejection ability. In order to improve the control performance of the plant protection UAV, the application research of robust backstepping control strategy is carried out, and the Robust Backstepping Attitude Controller (RBAC) is designed to force the quadrotor to follow the desired attitude. Through simulation experiments, the control effect of RBAC and the Backstepping Terminal Sliding Mode Controller (BTSMC) in the literature are compared and analysed. The simulation results show that: RBAC can improve the dynamic performance of the system. The average settling time of the system is shortened by 404.663ms, which is 24.85% faster than that of the BTSMC system. Simultaneously, the system is insensitive to external unknown disturbance and has strong robustness.

Evaluation and Experiment of Flight Parameter Quality of the Plant Protection UAV Based on Laser Tracker

Research shows that the accurate acquisition of flight parameters of the plant protection UAV and accurate evaluation of flight parameter quality have great significance for improving the effect and precision of spraying. In order to further improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, this study conducted an evaluation and experiment of the flight parameter quality of the plant protection UAV using a laser tracker. The experimental results showed that the current plant protection UAV used the average altitude and speed of the onboard sensors to determine whether it reached the preset flight operation parameters, but this interpretation method could not accurately reflect the actual flight situation. Laser trackers could obtain more accurate flight parameters, especially instantaneous flight parameters. Compared with the laser tracker, the flight trajectory, altitude, and speed of the UAV reflected by onboard sensors were erroneous and tended to be smooth and stable. This method can obtain more accurate flight parameters, improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, and provide data support and a reference for the precision spraying and performance improvement of the plant protection UAV.

Reflectance Estimation from Multispectral Linescan Acquisitions under Varying Illumination—Application to Outdoor Weed Identification

To reduce the amount of herbicides used to eradicate weeds and ensure crop yields, precision spraying can effectively detect and locate weeds in the field thanks to imaging systems. Because weeds are visually similar to crops, color information is not sufficient for effectively detecting them. Multispectral cameras provide radiance images with a high spectral resolution, thus the ability to investigate vegetated surfaces in several narrow spectral bands. Spectral reflectance has to be estimated in order to make weed detection robust against illumination variation. However, this is a challenge when the image is assembled from successive frames that are acquired under varying illumination conditions. In this study, we present an original image formation model that considers illumination variation during radiance image acquisition with a linescan camera. From this model, we deduce a new reflectance estimation method that takes illumination at the frame level into account. We experimentally show that our method is more robust against illumination variation than state-of-the-art methods. We also show that the reflectance features based on our method are more discriminant for outdoor weed detection and identification.

Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits

Reducing risk from pesticide applications has been gaining serious attention in the last few decades due to the significant damage to human health, environment, and ecosystems. Pesticide applications are an essential part of current agriculture, enhancing cultivated crop productivity and quality and preventing losses of up to 45% of the world food supply. However, inappropriate and excessive use of pesticides is a major rising concern. Precision spraying addresses these concerns by precisely and efficiently applying pesticides to the target area and substantially reducing pesticide usage while maintaining efficacy at preventing crop losses. This review provides a systematic summary of current technologies used for precision spraying in tree fruits and highlights their potential, briefly discusses factors affecting spraying parameters, and concludes with possible solutions to reduce excessive agrochemical uses. We conclude there is a critical need for appropriate sensing techniques that can accurately detect the target. In addition, air jet velocity, travel speed, wind speed and direction, droplet size, and canopy characteristics need to be considered for successful droplet deposition by the spraying system. Assessment of terrain is important when field elevation has significant variability. Control of airflow during spraying is another important parameter that needs to be considered. Incorporation of these variables in precision spraying systems will optimize spray decisions and help reduce excessive agrochemical applications.

Analysis of Subdivision Automatic Control Precision Spraying

In this paper, based on the fact that is still a small peasant economy in China and there are many small plots, this article studies small and medium sprayers. In this sprayer, the subdivision precision spraying control system, designed for precision agriculture applications, was simulated by the LabVIEW software, while an experimental setup was able to measure and record during laboratory experiments. The main pipeline of sprayer chose the A and B. And the two different pipelines were set in the different target spray volume. When the theoretical spray value was kept being unchanged, the flow rate was verified with the field sprayer speed which were set by the pulse generator. With that the pressure stability test was completed. Based on the analysis and experimental results, the flow control precision is 97.03%, and the pressure stability precision is 97.88%, the relative average of the pulse generator is 0.05. Finally, the subdivision system could control the flow of the two branches and was better than the traditional spray method in China, while it could achieve more precise control of spray.

Design and development of an unmanned aerial vehicle for agricultural spraying in Brazil

This paper presents the development of the unmanned aerial vehicle (UAV) and its configurations as a platform for agricultural sprayers, with a hopper with a capacity of 100 kg, which can perform better maneuvers than conventional agricultural aviation, for precision spraying on small and medium Brazilian properties agricultural. The development and construction focused on precision spray agriculture, taking into account the reduction of costs and accident risks, modernizing, and complementing the activity. Prince Air Models Ltd. made the prototype with resources from FAPESP under Brazilian patent number PI 0404045-7 B1. It presented acceptable results for all flight situations requested with 100 kg of payload and flying in typical maneuvers and agricultural patterns.

Field Weed Recognition Based on an Improved VGG With Inception Module

The precision spraying of herbicides can significantly reduce herbicide use, and recognizing different field weeds is an important part of it. In order to enhance the efficiency and accuracy of field weed recognition, this article proposed a field weed recognition algorithm based on VGG model called VGG Inception (VGGI). In this article, three optimizations were made. First, the reduced number of convolution layers to reduce parameters of network. Then, the Inception structure was added, which can maintain the main features, and have better classification accuracy. Finally, data augmentation and transfer learning methods were used to prevent the problem of over-fitting, and further enhance the field weed recognition effect. The Kaggle Images dataset was used in the experiment. This work achieved greater than 98% precision in the detection of field weeds. In actual field, the accuracy could reach 80%. It indicated that the VGGI model has an outstanding identification performance for seedling, and has significant potential for actual field weed recognition.

Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.