Leaf Area Estimation by Semantic Segmentation of Point Cloud of Tomato Plants
Takeshi Masuda
Abstract
Growth monitoring is an essential task in agriculture for obtaining good crops and sustainable management of cultivation. Though it is essential, it is also a hard task requiring much labor and working time, and many automation approaches have been proposed. We present an attempt to estimate the leaf area of the tomatoes grown in a sunlight-type plant factory. We scanned tomato plants by an RGB-D sensor that moves vertically to scan one side of the plants from the pathway. We built a point cloud by merging the scanned data, and we segmented it into four classes (Stem, Leaf, Fruit, and Other) based on annotation. With a limited amount of data, we estimated the stem from Stem points, and from the number of Leaf points around the stem, we estimate the leaf area of a specific tomato plant in a plant factory with the relative error of about 20%.