Deep Neural Frameworks for Plant Disease Recognition and Categorization

Manesh Prakashrao Patil; Monika Tripathi Tripathi

doi:10.36948/ijfmr.2025.v07i06.62653

Deep Neural Frameworks for Plant Disease Recognition and Categorization

Author(s)	Mr. Manesh Prakashrao Patil, Prof. Dr. Monika Tripathi Tripathi
Country	India
Abstract	Detecting plant diseases is essential to contemporary agriculture since it allows for early intervention to increase crop output and reduce financial losses. Recent developments in deep learning (DL) and machine learning (ML) have shown great promise for automating the detection of diseases using sensor and visual data. Convolutional neural networks (CNNs), ResNet, DenseNet, U-Net, Mask R-CNN, and YOLO are examples of state-of-the-art DL architectures that are frequently used for extracting and learning hierarchical features from leaf pictures, hyperspectral images, and other multimodal plant datasets. This paper reviews developments in the field from 2015 to 2022. Prominent datasets like PlantVillage, Agri-Vision, and PlantDoc have made it easier to compare and test different models. Using a hybrid convolutional backbone based on EfficientNet-B7 to strike a compromise between high representational capacity and computational economy, we offer a repeatable framework that combines many cutting-edge methods for better illness detection and classification. Multiscale dilated convolutions are used to capture multiscale disease patterns, enhancing feature representations without adding more computational overhead, and adaptive segmentation mechanisms allow precise lesion localization for region-level analysis that supports severity assessment and classification. The framework emphasizes repeatability and practical application for research and industrial usage, and it is accompanied by comprehensive methodological explanations, algorithm pseudocode, and illustrated diagrams and flowcharts. In addition, the paper addresses common issues in plant disease detection, such as the lack of labeled datasets, inter-domain variability, class imbalance, and hardware constraints for real-time deployment. It also discusses mitigation strategies, such as transfer learning from pre-trained models, generative adversarial network (GAN)-based data augmentation, and model compression techniques for optimal edge deployment. In order to direct future research and real-world application, evaluation measures, performance analyses, and robustness concerns are also included. Overall, this survey and suggested methodology offer a thorough overview of current developments and solutions in ML- and DL-based plant disease detection. They show how integrating hybrid convolutional architectures, multiscale dilations, and adaptive segmentation can improve detection accuracy while addressing practical limitations, providing a scalable approach for precision agriculture and assisting researchers, agronomists, and practitioners in creating dependable, effective, and repeatable plant disease detection systems.
Keywords	Plant disease detection, deep learning, convolutional neural networks, segmentation, transfer learning, EfficientNet
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-06
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.62653

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Deep Neural Frameworks for Plant Disease Recognition and Categorization

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