Development of a Three-Dimensional Plant Localization Technique for Automatic Differentiation of Soybean from Intra-Row Weeds

• Published in: Agriculture (Basel) Vol. 12; no. 2; p. 195
• Main Authors: Su, Wen-Hao, Sheng, Ji, Huang, Qing-Yang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Algorithms; Cameras; computer vision; crop signaling; Crops; Differentiation; Economics; Environmental impact; Fluorescence; fluorescent imaging; Herbicides; Labor costs; Legumes; Lettuce; Light emitting diodes; Localization; Machine learning; Morphology; Permeability; plant localization; precision agriculture; Profitability; Rhodamine; Seed treatments; Seedlings; Seeds; Signaling; Soybeans; Technology; Weed control; Weeds; United States > US; Japan
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture12020195

Soybean is a legume that is grown worldwide for its edible bean. Intra-row weeds greatly hinder the normal growth of soybeans. The continuous emergence of herbicide-resistant weeds and the increasing labor costs of weed control are affecting the profitability of growers. The existing cultivation technology cannot control the weeds in the crop row which are highly competitive with the soybean in early growth stages. There is an urgent need to develop an automated weeding technology for intra-row weed control. The prerequisite for performing weeding operations is to accurately determine the plant location in the field. The purpose of this study is to develop a plant localization technique based on systemic crop signalling to automatically detect the appearance of soybean. Rhodamine B (Rh-B) is a signalling compound with a unique fluorescent appearance. Different concentrations of Rh-B were applied to soybean based on seed treatment for various durations prior to planting. The potential impact of Rh-B on seedling growth in the outdoor environment was evaluated. Both 60 and 120 ppm of Rh-B were safe for soybean plants. Higher doses of Rh-B resulted in greater absorption. A three-dimensional plant localization algorithm was developed by analyzing the fluorescence images of multiple views of plants. The soybean location was successfully determined with the accuracy of 97%. The Rh-B in soybean plants successfully created a machine-sensible signal that can be used to enhance weed/crop differentiation, which is helpful for performing automatic weeding tasks in weeders.