Leaf Nitrogen Classification of Common Bean (Phaseolus Vulgaris L.) Using Deep Learning Models and Images

Beans are a legume that is widely grown and consumed globally, being the staple food for humans in developing countries. Nitrogen (N) is the most limiting nutrient for yield and foliar analysis is crucial to ensure a balanced nitrogen fertilization. However, conventional methods are time-consuming, requiring new technologies to optimize the supply of N. In this work, the performance of two deep learning models in the classification of leaf nitrogen in beans using RGB images was evaluated and compared. The BRS Estilo was used in a greenhouse in a completely randomized design with 4 doses of nitrogen (0, 25, 50 e 75 kg N ha-1) and 12 reps. The image bank was composed of 4 subfolders, each containing 500 images of 224x224 pixels obtained from plants grown under different doses of N. Matlab© R2022b were used for processing of the models. The performance of ResNet-50 was superior when compared to CNN, with an accuracy test of 84%, while the value observed for CNN was 82%. The use of images combined with deep learning can be a promising alternative to slow laboratory analyses, optimizing the estimation of leaf N and providing a rapid intervention by the producer to achieve higher productivity.