Early Detection of Red Palm Weevil in Agricultural Environment Using Deep Learning

Abstract

The red palm weevil (RPW) represents a significant danger to palm trees farms all over the world, which will result in considerable financial losses. The absence of apparent signs until the death of the palm tree makes it difficult to identify RPW infections at an early stage. The prompt detection of RPW diseases is further complicated by large-scale farms. In order to accomplish early detection of RPW using image analysis, this paper proposed a RPW classification model based on the proposed modified ResNet-34 deep learning architecture. A dataset of 483 images is used to assess the model’s performance. For the assessment, two different dataset settings are used. In the initial dataset setup, images are divided into three groups: adults, eggs, and Pupae. Four additional categories are added to the classification in the second dataset setup: female adults, male adults, eggs, and pupae. Experimental findings show the usefulness of the proposed model, with a remarkable total accuracy of 98% for both dataset setups. These results highlight the value of using the modified ResNet-34 architecture for the early detection of RPW. Moreover, the findings demonstrated that the proposed model offers great potential for decreasing the negative effects of RPW on palm tree farms and preventing financial losses in the agriculture sector.