# Design and Experiment of Variable-Spray System Based on Deep Learning
###### Abstract  
In response to issues of the low pesticide-utilization rate caused by the traditional constant spraying method, a variable-spraying system for wheat-field weeks was proposed in this study based on real-time segmentation by deep learning. In this study, the weed density within the operational area was obtained by using the improved DeepLab V3+ semantic segmentation mode, and a variable spray-level model based on real-time weed density and speed was constructed by using PWM variable-spraying technology to adjust the spray volume. The lightweight MobileNet V2 network was selected as its backbone network, and the CA attention mechanism was integrated into the feature extraction module. The mean intersection over the union (MIoU) and mean pixel accuracy (MPA) of the improved DeepLab V3+ were 73.34% and 80.76%, respectively, and the segmentation time for a single image was 0.09 s. The results of field verification tests showed that (1) compared with constant spraying, variable spraying can increase droplet density and save the amount of pesticides, with the droplet density increased by 38.87 droplets/cm\({}^{2}\) and the pesticide consumption saved by 46.3%; and (2) at the same speed, the average droplet-coverage rate in the areas with sparse weed density decreased by 13.98% compared with the areas with dense weeds. Under the same plant density, the average coverage rate of 0.5 m/s increased by 2.91% and 6.59% compared with 1 m/s and 1.5 m/s, respectively. These results further demonstrated that the system can automatically adjust the spray volume based on different travel speeds and weed densities. This research can provide theoretical and reference support for the development of new precision-spray plant-protection machinery for wheat fields. Keywords:wheat; deep learning; DeepLab V3+; variable spray +
Footnote †: journal: MedicalSVRS. [PERSON] et al. [15] proposed a vision tracking method based on machine vision to meet the requirements of precision spraying large-spaced crops. The experimental results showed that the target spray error was less than or equal to 6.5 mm, and the target precision spraying was realized. [PERSON] et al. [16] used machine learning and the color index to distinguish weeds in fields based on multi-spectral images of farmland. The results showed that 71.7%-95.4% and 4.3%-12% of herbicides were saved in the untreated areas and low-herbicide-dosage areas, respectively. [PERSON] et al. [17] designed a variable-spraying system that identified the crops in the field and between the ridges based on machine vision and adjusted the spray flow by adjusting the spray width of the sprayer. The experiment results indicated that the error between the spray amount of the variable-spraying system and the theoretical value was less than 10%. [PERSON] et al. [18] created and evaluated a cost-effective intelligent sprayer using a laboratory-scale YOLO model of spraying agricultural chemicals on weeds. It was reported that the accuracy of using different Nvidia GPUS ranged from 40% to 90%. In summary, some research has been conducted on variable-spray technology, but there is little research on the variable spraying of wheat. In this study, the weed density in wheat fields was analyzed in real time based on variable-spraying techniques with visual sensors, and a variable-spraying flow-regulation model, with real-time weed density and the unit speed as the control variables, was established. Firstly, based on an improved DeepLab V3+ semantic segmentation model, the weed density in the field was obtained in real time. Secondly, based on the flow-regulation model, a system-performance test experiment was conducted. Finally, the effectiveness of the variable-spraying system was verified by field tests, and under different speed conditions, more spraying was realized in the areas with dense weeds; less spraying was realized in the areas with sparse weeds; and no spraying was realized in the areas without weeds.