Automated lepidopteran pest developmental stages classification via transfer learning framework

The maize crop is highly susceptible to damage caused by its primary pests, which poses considerable challenges in manually identifying and controlling them at various larval developmental stages. To mitigate this issue, we propose an automated classification system aimed at identifying the different larval developmental stages of 23 instars of 4 major lepidopteran pests: the Asian corn borer, Ostrinia furnacalis (Guenée; Lepidoptera: Crambidae), the fall armyworm, Spodoptera frugiperda (J.E. Smith; Lepidoptera: Noctuidae), the oriental armyworm, Mythimna separata (Walker; Lepidoptera: Noctuidae), and the tobacco cutworm, Spodoptera litura (Fabricius; Lepidoptera: Noctuidae). Employing 5 distinct Convolutional Neural Network architectures-Convnext, Densenet121, Efficientnetv2, Mobilenet, and Resnet-we aimed to automate the process of identifying these larval developmental stages. Each model underwent fine-tuning using 2 different optimizers: stochastic gradient descent with momentum and adaptive moment estimation (Adam). Among the array of models tested, Densenet121, coupled with the Adam optimizer, exhibited the highest classification accuracy, achieving an impressive 96.65%. The configuration performed well in identifying the larval development stages of all 4 pests, with precision, recall, and F1 score evaluation indicators reaching 98.71%, 98.66%, and 98.66%, respectively. Notably, the model was ultimately tested in a natural field environment, demonstrating that Adam_Densenet121 model achieved an accuracy of 90% in identifying the 23 instars of the 4 pests. The application of transfer learning methodology showcased its effectiveness in automating the identification of larval developmental stages, underscoring promising implications for precision-integrated pest management strategies in agriculture.