Real-time control of high-resolution micro-jet sprayer integrated with machine vision for precision weed control

被引:18
作者
Raja, Rekha [1 ]
Slaughter, David C. [1 ]
Fennimore, Steven A. [2 ]
Siemens, Mark C. [3 ]
机构
[1] Univ Calif Davis, Dept Biol & Agr Engn, Davis, CA 95616 USA
[2] Univ Calif Davis, Dept Plant Sci, Davis, CA 95616 USA
[3] Univ Arizona, Dept Biosyst Engn, Tucson, AZ 85721 USA
关键词
Precision agriculture; Micro-jet sprayer; Robotic weed control; Crop signalling; Artificial intelligence; CONTROL-SYSTEM; CLASSIFICATION; LETTUCE; IDENTIFICATION; LOCALIZATION; TOMATO;
D O I
10.1016/j.biosystemseng.2023.02.006
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
The advent of automated technology in agriculture employing robots allows researchers and engineers to automate many of the tasks in a semi-structured, natural farming envi-ronment where these tasks need to be performed. Here we propose a fast-intelligent weed control system using a crop signalling concept with machine vision and a precision micro-jet sprayer to target in-row weeds for precision herbicide application. Crop signalling is a novel technology invented to read crop plants by machine to simplify the task of differ-entiating vegetable crops from weeds for selective weed control in real-time. In-row weed control in vegetable crops like lettuce requires a very precise herbicide spray resolution with a fast response time. A novel, accurate, high-speed, centimetre precision spray tar-geting actuator system was designed and experimentally validated in synchronization with a machine vision system to spray detected weeds located between lettuce plants. The system processed an image, representing a 120 mm x 180 mm region of row-crop in 80 ms, which allowed the micro-jet sprayer to successfully function at a travel speed of 3.2 km h-1 and selectively deliver herbicide to the weed targets. The analysis of the overall perfor-mance of the system to kill weeds in indoor experimental trials is discussed and presented. Findings indicate that 98% weeds were correctly sprayed which indicates the efficacy and robustness of the proposed systems.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of IAgrE. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页码:31 / 48
页数:18
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