Application-Specific Evaluation of a Weed-Detection Algorithm for Plant-Specific Spraying

Abstract

Robotic plant-specific spraying can reduce herbicide usage in agriculture while minimizing labor costs and maximizing yield. Weed detection is a crucial step in automated weeding. Currently, weed detection algorithms are always evaluated at the image level, using conventional image metrics. However, these metrics do not consider the full pipeline connecting image acquisition to the site-specific operation of the spraying nozzles, which is vital for an accurate evaluation of the system. Therefore, we propose a novel application-specific image-evaluation method, which analyses the weed detections on the plant level and in the light of the spraying decision made by the robot. In this paper, a spraying robot is evaluated on three levels: (1) On image-level, using conventional image metrics, (2) on application-level, using our novel application-specific image-evaluation method, and (3) on field level, in which the weed-detection algorithm is implemented on an autonomous spraying robot and tested in the field. On image level, our detection system achieved a recall of 57% and a precision of 84%, which is a lower performance than detection systems reported in literature. However, integrated on an autonomous volunteer-potato sprayer-system we outperformed the state-of-the-art, effectively controlling 96% of the weeds while terminating only 3% of the crops. Using the application-level evaluation, an accurate indication of the field performance of the weed-detection algorithm prior to the field test was given and the type of errors produced by the spraying system was correctly predicted.

Reduction of pesticide application via real-time precision spraying

“…Sensors on agricultural machines, drones, or high-resolution satellite-based platforms play a central role in this process. Site-specific spraying can reduce herbicide usage by up to 70% while maintaining 100% weed control 27 .

Figure 3 Spatial variability of the sprayer boom application throughout the field in pre-planting operation.

…”

Advances in site‐specific weed management in agriculture—A review

“…Swarms of small, low‐cost robots can be effectively and economically used preferentially in high‐value row crops with a low weed control threshold and in organic farming. Various prototypes of spraying robots for precise spot spraying in individual crops have been developed and analysed (Ruigrok et al, 2020; Utstumo et al, 2018; Wu et al, 2020). They achieved more than 80% correct image classification and up to 96% weed control while <10% crop damage.…”

“…Various prototypes of spraying robots for precise spot spraying in individual crops have been developed and analysed (Ruigrok et al, 2020;Utstumo et al, 2018;Wu et al, 2020). They achieved more than 80% correct image classification and up to 96% weed con- directed around the crop plants using an RTK-GNSS-controlled guidance system (Nørremark et al, 2012).…”

“…Current advances of this concept comprise several commercial and practical applications in camera‐guided weed hoeing, sensor‐based patch and spot spraying, robotic weeding and weed scouting (Champ et al, 2016; Dyrmann et al, 2016; Gutjahr et al, 2012; Kunz et al, 2015; Peteinatos et al, 2020; Tillett et al, 2002). Several more prototypes, such as sensor‐based electrical weed control (Reiser et al, 2019), Unmanned Aerial Vehicle (UAV)‐based weed mapping and patch spraying (Mink et al, 2018; Rasmussen et al, 2013) and targeted weed control with direct injection of herbicides (Pohl et al, 2019; Ruigrok et al, 2020), are currently being developed.…”

“…Several more prototypes, such as sensor-based electrical weed control (Reiser et al, 2019), Unmanned Aerial Vehicle (UAV)-based weed mapping and patch spraying (Mink et al, 2018;Rasmussen et al, 2013) and targeted weed control with direct injection of herbicides (Pohl et al, 2019;Ruigrok et al, 2020), are currently being developed.…”