Towards Smart Agriculture: Deep Learning Methods for Plant Disease Monitoring

Author: Apoorva Dwivedi, Pharindra Kumar Sharma, Gourav Kumar Sharma
Published Online: July 1, 2026
DOI: http://doi.org/10.63766/spujstmr.26.000088
Abstract
References

Plant diseases are one of the most disastrous threats to global agriculture, causing crop loss and decreasing food security. Although early and accurate detection of plant diseases is one of the most important aspects of disease management, it is often performed using traditional methods including visual examination and laboratory testing, which are time-consuming, subjective and resource-intensive. Deep learning has been a groundbreaking technology during the past few years due to its role in switching smart agriculture into an automatic, precise and scalable solution for the identification of plant diseases and monitoring.This paper aims at exploring the deep learning methods using Plant disease detection as the base but will mainly focus on image-based systems providing a comprehensive survey of modern Plant disease detection. Specifically, it investigates CNNs, RNNs, GANs and transformer-based architectures for diagnosing various diseases in different kinds of crops. The importance of transfer learning, data augmentation as well as synthetic data generation to improve the performance and generalizability of models is extensively discussed here.Paper also discusses the most commonly used datasets, such as PlantVillage, and investigates how they might help in a benchmark of model performance. Problems like lack of annotated data, overfitting, lack of adversarial robustness and going off smooth explainable models. Finally, the paper discusses stateof-the-art functionalities like edge computing, lightweight neural networks, multimodal learning, and explainable AI (XAI) that are necessary to deploy real-world applications.This review synthesizes existing work—especially recent scientific literature—and highlights gaps to provide researchers, practitioners, and policymakers a wide-ranging reference for the integration of deep learning into precision agriculture as it pertains to sustainable and resilient crop production.

Keywords: Plant disease detection, Deep learning, Smart agriculture, Convolutional neural networks (CNN), Image-based classification, Transfer learning, precision farming.
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