Classification of soybean pods using the deep learning techniques

Abstract

Crop productivity estimate aims at the economic definitions about crop, agricultural management, and land use, among others. However, it is common to observe the use of visual methods to estimate the productivity of the soybean crop through the classification of pods, resulting in a slow, costly method besides being susceptible to human errors. Thus, the objective of this work was to carry out the training of two deep learning methods to classify soybean pods according to the number of grains based on images obtained using a smartphone. Data collection was carried out at the Federal University of Viçosa (UFV). Data consisted of capturing images from a smartphone and training two deep learning models: Mask R-CNN and YOLOv4. To capture the images, the soybean pods were pulled from the plants and placed in a white-bottom container. This procedure occurred for each plant collected. Both models tended towards a better classification for the two- and three-grain pods, reaching a value of 90% for the F1 score metric. This fact may have occurred because of the greater amount of these two types of pods present in the chosen cultivars. Finally, the potential of using deep learning to classify soybean pods based on the number of grains was observed.