A Comprehensive Review on Plant Leaf Disease Detection Systems Using Machine Learning and Deep Learning Techniques

Digital Technologies Research and Applications

Review

A Comprehensive Review on Plant Leaf Disease Detection Systems Using Machine Learning and Deep Learning Techniques

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https://doi.org/10.54963/dtra.v5i1.1746
Katuri, K. N., & Medabalimi, S. R. (2026). A Comprehensive Review on Plant Leaf Disease Detection Systems Using Machine Learning and Deep Learning Techniques. Digital Technologies Research and Applications, 5(1), 297–310. https://doi.org/10.54963/dtra.v5i1.1746
Volume 5 Issue 1: March 2026 (in progress)
Copyright (c) 2026 Keerthi Naidu Katuri, Subba Rao Medabalimi
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Authors

  • Keerthi Naidu Katuri

    Department of Computer Science and Engineering, Annamacharya University, Rajampet 516115, India
  • Subba Rao Medabalimi

    Department of Computer Science and Engineering, Annamacharya University, Rajampet 516115, India

Received: 22 October 2025; Revised: 1 December 2025; Accepted: 12 December 2025; Published: 24 March 2026

The problems of plant leaf disease are rather serious in the world agricultural industry, leading to a significant decrease in crop quantity and quality, consequently, resulting in a huge loss in the economy and food insecurity. Detection and successful classification of plant diseases at the initial stage is essential to further agricultural output and the quality production of food. The recent improvements in the field of artificial intelligence (AI), specifically, machine learning (ML) and deep learning (DL), have shown significant prospects in automating and enhancing methods of diagnosing plant leaf diseases by using a wide variety of ML and DL algorithms. This review article presents an in-depth analysis of thirty novel methods created by researchers to diagnose and classify plant leaf diseases. They are such conventional classifiers as Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN), along with some advanced DL architectures, such as Convolutional Neural Networks (CNN), VGG16, ResNet50, InceptionResNetV2, EfficientNet, and various hybrids. The review analysis takes into consideration the methodologies applied, performance metrics, and insights in practice, as well as the strengths and weaknesses of both. The most crucial findings of the review show that deep learning models, and CNNs in particular, tend to be more accurate, robust, and feature extractors than traditional models of MLs. The performance of classification is also enhanced by numerous hybrid models that will use ML together with DL, and transfer learning has been an effective method to enhance the generalization using small datasets. Nevertheless, with all this progress, the issues of diversity of datasets, computational resource requirements and model interpretability are still to be explored in the future.

Keywords:

Image Classification Smart Agriculture Precision Farming Crop Disease Diagnosis Agricultural Monitoring Systems Feature Extraction Hybrid Models Artificial Intelligence in Agriculture

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