Estimating soybean leaf defoliation using convolutional neural networks and synthetic images

Abstract Agriculture plays an important role in the economy of several countries, contributing from the production of food and income to the generation of jobs. To improve productivity in agriculture, proper crop management should be accomplished through pest control. One approach is to monitor the defoliation level, that is, the percentage of leaf damaged by insects. Despite the importance, this monitoring is performed manually most of the time, which affects reliability as well as it is considered to be a time-consuming task. In this paper, we propose a new fully-automatic method to estimate the defoliation level based on convolutional neural networks (CNN). The main CNN architectures (AlexNet, VGGNet and ResNet) were adapted from classification to regression by replacing the softmax layer with a fully-connected layer with one neuron and a sigmoid activation function. Since CNNs require a large number of training examples, this paper also proposes approaches for generating synthetic defoliation images. In this way, our method is trained only with synthetic images and evaluated using real images. In the experiments, we obtained a root mean square error of only 4.57 even for images with severe defoliation. Additionally, we presented experimental evidence that the proposed method reconstructs the damaged leaf parts to then estimate the defoliation level.