Towards practical object detection for weed spraying in precision agriculture - Darbyshire, Madeleine; Salazar-Gomez, Adrian; Gao, Junfeng; Sklar, Elizabeth I.; Parsons, Simon (Frontiers in Plant Science, 2023)
Weeds pose a persistent threat to farmers' yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to 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 trialling 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 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 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.