Design of field real-time target spraying system based on improved YOLOv5

He, Lingbing; Guo, Changle; Yang, Zishang; Chai, Jiajun; Shi, Yong; Liu, Jiawei; Zhang, Kaifei; Liu, Daoqi; Xu, Yufei

doi:10.3389/fpls.2022.1072631

Cited by 13 publications

(1 citation statement)

References 39 publications

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“…In Ruigrok et al (2020), YOLOv3 was integrated into a spraying system, and the hit rate was assessed in sugar beet and potato fields by evaluating the location of wet patches in the soil left by the sprayer. Similarly, Li et al (2022) integrated YOLOv5 detection into a spraying system and assessed the hit rate using water-sensitive paper. These approaches provide an insight into the weed hit rat given a particular detector and a spraying system in a particular location.…”

Section: Related Workmentioning

confidence: 99%

Towards practical object detection for weed spraying in precision agriculture

Darbyshire,

Salazar-Gomez,

Gao

et al. 2023

Front. Plant Sci.

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Weeds pose a persistent threat to farmers’ yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to the surrounding ecosystems. Precision spraying aims to reduce harms to the surrounding environment by targeting only the weeds rather than spraying the entire field with herbicide. Such an approach requires weeds to first be detected. With the advent of convolutional neural networks, there has been significant research trialing such technologies on datasets of weeds and crops. However, the evaluation of the performance of these approaches has often been limited to the standard machine learning metrics. This paper aims to assess the feasibility of precision spraying via a comprehensive evaluation of weed detection and spraying accuracy using two separate datasets, different image resolutions, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is proposed to compare the performance of different detection algorithms while varying the precision of the spray nozzles. The key performance indicators in precision spraying that this study focuses on are a high weed hit rate and a reduction in herbicide usage. This paper introduces two metrics, namely, weed coverage rate and area sprayed, to capture these aspects of the real-world performance of precision spraying and demonstrates their utility through experimental results. Using these metrics to calculate the spraying performance, it was found that 93% of weeds could be sprayed by spraying just 30% of the area using state-of-the-art vision methods to identify weeds.

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Section: Related Workmentioning

confidence: 99%