Detection and classification of soybean pests using deep learning with UAV images

Abstract This paper presents the results of the evaluation of five deep learning architectures for the classification of soybean pest images. The performance of Inception-v3, Resnet-50, VGG-16, VGG-19 and Xception was evaluated for different fine-tuning and transfer learning strategies over a dataset of 5,000 images captured in real field conditions. The experimental results showed that the deep learning architectures trained with a fine-tuning can lead to higher classification rates in comparison to other approaches, reaching accuracies of up to 93.82%. In addition, deep learning architectures outperformed traditional feature extraction methods, such as SIFT and SURF with Bag-of-Visual Words approach, the semi-supervised learning method OPFSEMImst, and supervised learning methods used to classify images, for example, SVM, k-NN and Random Forest. The results indicate that architectures evaluated can support specialists and farmers in the pest control management in soybean fields.