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Global food security is critically threatened by plant diseases, which can be especially catastrophic for smallholder farmers whose economic survival hinges on a productive groundnut crop. It is therefore vital to engineer an algorithm capable of early, automated plant disease diagnosis. Working alongside a pathologist, a large-scale dataset of groundnut leaf imagery was assembled to enable this automated detection. In image classification tasks, Convolutional Neural Networks have demonstrated remarkable efficiency. This investigation constructs a disease identification and classification system using a tri-CNN architecture that incorporates DenseNet169, Inception, and Xception, all of which were initially trained on the ImageNet database. The proposed ensemble strategy formulates final predictions for test data by applying two custom nonlinear functions to the decision outputs of these base learners, integrating their scores across four standard performance measures: recall, precision, accuracy, and F1-score. The tailored CNN models are thus utilized to train our system for superior results. The methodology was tested on a real-world groundnut leaf dataset. The introduced model registered an accuracy reading of 98.46%.

In some Asian countries, jute is a fundamental source of revenue and contributes to Gross Domestic Product (GDP). Like other crops, jute is vulnerable to pest invasions, and detecting them often relies on visual inspection in countries such as Bangladesh, India, Myanmar, and China. This practice is also slow, difficult, and somewhat inaccurate, posing a significant economic risk. To mitigate this challenge, the investigation proposes a highly accurate and resilient transfer learning (TL)- based JutePestDetect model for detecting jute pests at their initial stages. To begin with, we assembled a jute pest collection comprising 17 categories and roughly 380 images per category, which were scrutinized after manual and automated preprocessing and refinement, including background removal and scaling. Following this, five distinguished pre-trained architectures—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were picked from an earlier work to formulate the JutePestDetect model. Every architecture was adjusted by replacing the classification layer with a global average pooling layer and inserting a dropout layer for regularization. For performance measurement, a range of indicators, including precision, recall, F1 score, ROC curve, and confusion matrix, were used. These examinations provided a deeper understanding of the models’ capabilities. Within this set, the proposed JutePestDetect model, based on a tailored regularized DenseNet201, outperformed its counterparts, achieving an outstanding accuracy of 99%. Hence, our suggested method and tactics deliver a refined solution for pest recognition specific to jute, with the potential to substantially assist cultivators worldwide.