JutePestDetect: An Intelligent Approach for Jute Pest Identification Using Fine-Tuned Transfer Learning

TL;DR

This paper introduces JutePestDetect, a transfer learning-based model that accurately identifies jute pests early, using a dataset of 17 pest classes, achieving 99% accuracy, to aid farmers in pest management.

Contribution

The study develops a novel transfer learning approach with fine-tuned models for early jute pest detection, outperforming existing methods with high accuracy.

Findings

DenseNet201-based model achieved 99% accuracy.

The model effectively distinguishes 17 pest classes.

Transfer learning enhances pest detection efficiency.

Abstract

In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models -DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50 were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide.