Deep Neural Frameworks for Plant Disease Recognition and Categorization

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.

Plant phenotyping relevance

植物病害の画像認識・病斑局在化・重症度評価を扱うレビューであり、植物の病害状態を画像から抽出する方法論が中心です。

Code and data availability

The paper uses public benchmark datasets (PlantVillage, Plant Pathology 2020, AgriVision 2021) but provides no author-deposited code, trained models, or paper-specific data with a public URL. The only URLs present are citations to unrelated prior works by other authors, which do not qualify as paper-specific assets.

No evidence-backed public reproduction asset is currently recorded.