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Open AccessFeature PaperArticle

Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme

Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
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Robotics 2018, 7(3), 38; https://doi.org/10.3390/robotics7030038
Received: 4 June 2018 / Revised: 1 July 2018 / Accepted: 17 July 2018 / Published: 19 July 2018
(This article belongs to the Special Issue Agricultural and Field Robotics)
This paper proposes a scheme that combines computer vision and multi-tasking processes to develop a small-scale smart agricultural machine that can automatically weed and perform variable rate irrigation within a cultivated field. Image processing methods such as HSV (hue (H), saturation (S), value (V)) color conversion, estimation of thresholds during the image binary segmentation process, and morphology operator procedures are used to confirm the position of the plant and weeds, and those results are used to perform weeding and watering operations. Furthermore, the data on the wet distribution area of surface soil (WDAS) and the moisture content of the deep soil is provided to a fuzzy logic controller, which drives pumps to perform variable rate irrigation and to achieve water savings. The proposed system has been implemented in small machines and the experimental results show that the system can classify plant and weeds in real time with an average classification rate of 90% or higher. This allows the machine to do weeding and watering while maintaining the moisture content of the deep soil at 80 ± 10% and an average weeding rate of 90%. View Full-Text
Keywords: fuzzy logic; machine vision; field robotics; agriculture fuzzy logic; machine vision; field robotics; agriculture
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Chang, C.-L.; Lin, K.-M. Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme. Robotics 2018, 7, 38.

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