  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.
