A Deep-Learning Approach for Automatic Counting of Soybean Insect Pests

Abstract

The occurrence of insect pest attacks in soybean fields has worried farmers around the world. Early and automatic diagnosis of insect pests number could assess the infestation level of each plantation area to optimize the applications of pesticides in the crop and, consequently, reduce production costs and environmental impact. Recent research on insect count has adopted deep neural networks. However, researches have employed models trained to count only one species of insect, using images captured in a controlled environment, quite different from a real scenario. In order to obtain high accuracy, we evaluated three models of convolutional neural networks (CNNs) with three different training strategies: 100% fine-tuning with the weights obtained from ImageNet, a complete network with the weights initialized randomly and transfer learning with the weights obtained from ImageNet. Data augmentation and dropout were used during network training to reduce overfitting and increase generalization of the model. Our approach consists in segmenting an image from the plantation with the simple linear iterative clustering (SLIC) method and classifying each superpixel segment into a pest insect class using the CNN-trained classification model. The pest insect count is obtained by adding the insects of each superpixel class identified by our computer vision system. The results indicate that the deep-learning models can be used successfully to support specialists and farmers in the insect pest management in soybean fields.