Smart Crops Management Principles for Smarter Models: Survey of Deep Learning and Machine Learning Approaches for Leaf Disease Detection

Sandhya M; Dayana Sherine P M; Sabnam Pradhan

doi:10.36948/ijfmr.2026.v08i01.66844

Smart Crops Management Principles for Smarter Models: Survey of Deep Learning and Machine Learning Approaches for Leaf Disease Detection

Author(s)	Prof. Sandhya M, Prof. Dayana Sherine P M, Prof. Sabnam Pradhan
Country	India
Abstract	Plant diseases are a serious threat to crop yields and global food security. Catching them early and diagnosing them accurately is essential to prevent losses and support sustainable farming. This has been made possible due to breakthroughs in machine learning (ML), deep learning (DL), and computing which now contribute majorly towards automating, scaling, and speeding up the process of disease inference from visual data of the plant. This survey is a comprehensive examination of the recent progress in the application of ML and DL for the detection of leaf ailments, including the utilization of Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), transfer learning, and ensemble methods. The author evaluates the performance of these models based on accuracy, computational cost, data requirements, and robustness and considers hybrid pipelines that integrate ML and DL technologies such as CNN-SVM combinations, MobileNet families, and attention-driven designs including GANs and Vision Transformers. The improvement of the performance by data augmentation, feature extraction, and preprocessing is also covered by the work. The main body of work is a thorough review of more than 30 peer-reviewed research articles published from 2018 to 2024, identifying the pros and cons of methodologies and new trends explored in the studies. The review ends by highlighting the primary issues: data bias, on-the-fly implementation, and generalization to real-world scenarios and, at the same time, by indicating intriguing topics such as explainable artificial intelligence tailored for agricultural decision-making, federated learning for training with privacy preservation, and lightweight models for edge and mobile devices. Overall, this survey aims to be a handy resource for the researchers, practitioners, and agritech teams committed to elevating the state-of-the-art of smart farming through AI intervention for early and more reliable plant disease detection.
Keywords	Plant Disease Detection, Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNNs), Transfer Learning.
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-19
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.66844

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Smart Crops Management Principles for Smarter Models: Survey of Deep Learning and Machine Learning Approaches for Leaf Disease Detection

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