Pest attacks pose a substantial threat to jute production and other significant crop plants. Jute farmers in Bangladesh generally distinguish between different pests that appear to be the same using their eyes and expertise, which isn't always accurate. We developed an intelligent model for jute pests identification based on transfer learning (TL) and deep convolutional neural networks (DCNN) to solve this practical problem. The proposed DCNN model can realize fast and accurate automatic identification of jute pests based on photographs. Specifically, the VGG19 CNN model was trained by TL on the ImageNet database. A well-structured image dataset of four dominant jute pests is also established. Our model shows a final accuracy of 95.86% on the four most vital jute pest classes. The model’s performance is further demonstrated by the precision, recall, F1-score, and confusion matrix results. The proposed model is integrated into Android and IOS applications for practical uses.

害虫袭击对黄麻生产和其他重要农作物构成重大威胁。孟加拉国的黄麻种植者通常使用他们的眼睛和专业知识来区分看似相同的不同害虫，但这并不总是准确的。我们开发了一种基于迁移学习 (TL) 和深度卷积神经网络 (DCNN) 的黄麻害虫识别智能模型来解决这一实际问题。所提出的DCNN模型可以实现基于照片的黄麻害虫快速准确的自动识别。具体来说，VGG19 CNN 模型是由 TL 在 ImageNet 数据库上训练的。还建立了四种主要黄麻害虫的结构良好的图像数据集。我们的模型显示，四种最重要的黄麻害虫类别的最终准确率为 95.86%。模型的性能进一步体现在精度上，召回率、F1 分数和混淆矩阵结果。所提出的模型已集成到 Android 和 IOS 应用程序中以供实际使用。