Inter-Row Soybean Plantation Identification in Images to Support Automatic Alignment of a Weeder Machine - Panizzon, Jailson Lucas; Ortoncelli, André Roberto; Souza, Alinne C. Correa; Souza, Francisco Carlos M.; Oliveira, Rafael Paes de (Anais do Seminário Integrado de Software e Hardware (SEMISH); 2024: Anais do LI Seminário Integrado de Software e Hardware, 2024)
This study explores a Computer Vision approach to identify inter-row planting in soybean areas. Related work already explores the same problem, but our work differs by focusing on inter-row identification to support the alignment of weeding machines (commonly used by small farmers who produce organic products). We created an experimental database with images collected with a camera attached to a weeder. The planting lines and inter-rows were manually labeled. To detect planting lines and inter-rows, we use two segmentation algorithms based on Convolutional Neural Networks (Mask R-CNN and YOLACT), achieving an accuracy of up to 0.656 with the interpolation of the obtained segmentation results. The segmentation results obtained made it possible to estimate the inter-rows satisfactorily. We provide a database of collected images, with the planting lines and inter-rows noted. With these results, we intend to create a solution in future work that allows automatic alignment of the weeder. We also plan to develop similar solutions for other crops (in addition to the soybeans explored in the experiments).