Hiroyuki Ogata

信学技報, vol. 122, no. 385, IE2022-79, pp. 112-117, 2023年2月.

Under the Synecoculture environment, in which various plants are raised in mixed and dense vegetation, automatic maintenance of the field is difficult because of difficulties in separating each harvest. In this project, the situation in which one plant dominates the other plants is called “dominant situation”, and such dominant plants are to be cut. So, in this paper, we propose a method for detecting dominant plants from RGB images using deep learning. First, we partition the original image into small blocks. We perform VGG16 for each small block to predict the number of plants. If the number of the small blocks in each of which the number of plants is less than two exceeds the threshold, the original image is judged as a candidate of “dominant situation”. If the original image is judged as the candidate, similarity between dominant small blocks is computed using AKAZE, and if the similarity is high, the small blocks are judged to be in dominant situation. Experimental results show that high accuracies for estimating dominant situations are achieved.