Pests Classification Based on Convolutional Neural Networks in Smart Agriculture

The objective of this article is to design solutions for intelligent pest management in order to select the appropriate control method. The effectiveness of pest management strategies hinges on the accessibility of dependable and up-to-date information on the status of pest infestation. The classic approach for monitoring insect pests involves placing traps in areas where infestations are present, which are checked by human operators. This approach demands significant labor costs and offers limited spatial and temporal resolution. This proposal is based on an integrated protection logic and favors prevention and early detection of possible infestations. This article is dedicated to the automatic classification of images, using deep learning to develop a pest identification and recognition system. In order to carry out this classification, we implement two convolutional models (DenseNet121, VGG16). The idea of this proposal aims to develop an innovative smart trap capable of remotely monitoring harmful insects. The simulation results showed that the proposed Convolutional Neural Networks are effective for pests classification.