Robust Multi-Class Classification for Real-Time Agricultural Applications Using Efficient and Adaptive Deep Learning

G V Kartheek; Dr. D. Shobha Rani

doi:10.32628/IJSRST2411490

Authors

G V Kartheek M.Tech Scholar, Chadalawada Ramanamma Engineering College(A), Tirupati, India Author
Dr. D. Shobha Rani Professor, Department of CSE, Chadalawada Ramanamma Engineering College(A), Tirupati, India Author

DOI:

https://doi.org/10.32628/IJSRST2411490

Keywords:

Adaptive Augmented Learning, Convolutional Neural Networks, Plant Disease Classification, Precision Agriculture, Transfer Learning

Abstract

Plant diseases severely affect agricultural productivity, necessitating accurate and rapid detection methods. This research presents a robust, multi-class plant disease classification framework using adaptive deep learning. We utilize pre-trained convolutional neural networks (CNNs), specifically Xception, InceptionResNetV2, InceptionV3, ResNet50, and the proposed EfficientNetB3-based Adaptive Augmented Deep Learning (EfficientNetB3-AADL) model. Our approach leverages transfer learning combined with extensive data augmentation and trimming techniques to enhance model performance and mitigate overfitting. The EfficientNetB3-AADL architecture incorporates convolutional and max pooling layers, regularization strategies, and a dense feature learning layer, optimized to classify 52 disease categories from a publicly available leaf image dataset. The model’s performance is extensively evaluated using metrics such as accuracy, precision, recall, and F1 score. Notably, EfficientNetB3-AADL achieves superior accuracy over 98%, outperforming other CNN models. The proposed methodology highlights the efficacy of compound scaling and adaptive data augmentation in ensuring robust and efficient disease classification, suitable for real-time agricultural applications. This advancement supports sustainable farming by offering a scalable, computationally efficient solution for early and accurate disease detection in diverse crop species.

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