Classifying insect pests from image data using deep learning

Abstract

The fact that insecticidal pests impair significant agricultural productivity has become one of the main challenges in agriculture. Several prerequisites, however, exist for a high-performance automated system capable of detecting nuisance insects from massive amounts of visual data. We employed deep learning approaches to correctly identify insect species from large volumes of data in this study model and explainable AI to decide which part of the photos is used to categorize the insects from the data. We chose to deal with the large-scale IP102 dataset since we worked with a large dataset. There are almost 75,000 pictures in this collection, divided into 102 categories. We ran state-of-the-art tests on the unique IP102 data set to evaluate our proposed solution. We used five different Deep Neural Networks (DNN) models for image classification: VGG19, ResNet50, EfficientNetB5, DenseNet121, InceptionV3, and implemented the LIME-based XAI (Explainable Artificial Intelligence) framework. DenseNet121 outperformed all other networks, and we also implemented it to classify specific crop insect species. The classification accuracy ranged from 46.31 percent to 95.36 percent for eight crops. Moreover, we have compared our prediction to that of earlier articles to assess the efficacy of our research.