Pest detection and classification to reduce pesticide use in fruit crops based on deep neural networks and image processing

Abstract

The aim for organic farming is obtaining food of the highest quality, avoiding synthetic chemicals, protecting the environment and preserving the fertility of the land. In this context, effective pest control allows to reduce yield loss and pesticides application producing pollution-free vegetables. In fruit crops, Carpocapsa is the main pest present in pear, apple, walnut and quince trees. This insect produces irreversible damage to the fruit, since the larvae feed the seeds inside the fruit. In this paper, we present automatic pest detection and classification in the context of fruit crops based on image processing and Deep Neural Networks, employing an image collection obtained from in-field traps. Due to the limited size of the data set, we perform data augmentation to increase the number of images for training, to prevent over-fitting and to improve the deep neural network learning rate. Results showed an overall accuracy of 94.8%, while precision and recall scores for the class related with the moth were around 97.2% and 93.6% respectively, demonstrating the efficacy of this type of classifier proposed for pest detection. An inference time of 40 ms per image for the deep neural network classifier has been reached.