Jul 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 142-153· 0 citations· 25 references
TL;DR
The findings show that DL, especially CNN and transfer learning models, performed better than machine learning techniques, and points out several significant problems, such as dataset imbalance, insufficient generalization, computing inefficiency, and a dearth of real-world data.
Abstract
India’s economy is primarily based on agriculture. Agriculture has significant contribution in nation’s GDP. Food security and employment significantly influenced by agriculture. However factors like uncertain weather conditions, poor quality of seeds and plant diseases impact on agriculture productivity. Computer vision and DL algorithms are most crucial components of precision agriculture. Early detection can improve decision making, maximize pesticide use, and preserve harvests. Using CNN architectures, segmentation-based approaches, handcrafted feature-based methods, and hybrid approaches incorporating Machine Learning and Deep Learning this study seek to provide review of recent publications from 2020 to 2026. The review was carried out using a variety of publications with different datasets, methodologies, and outcomes. The findings show that DL, especially CNN and transfer learning models, performed better than machine learning techniques. It points out several significant problems, such as dataset imbalance, insufficient generalization, computing inefficiency, and a dearth of real-world data. Future research topics are also suggested which includes IoT-driven real-time solutions, lightweight architecture, domain adaption, and multimodal imaging. This review aims to develop plant disease detection technologies that are more dependable, scalable, and field deployable.
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T. S., K. U, Anusha Jajur J· World Journal of Advanced En...· 0 citations
Various convolutional neural network architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks.
Allupati Chakradhar Patro· International Journal of Sci...· 0 citations
The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.
Agricultural productivity and food security are heavily impacted by plant diseases, and thus there is a high demand for accurate and automated plant disease detection that can be achieved by applying deep learning techniques. This research proposes a Multi-Model Ensemble Method Based on Deep Learning for multi-plant disease detection using ResNet50 to improve classification performance across multiple crop varieties. The proposed framework takes five important categories of plants into consideration including tomato, potato, grape, apple and maize, and 10 classes of healthy and diseased plants are generated from the PlantSeg dataset. The Anaconda platform was used along with Python to create a development environment that allows data preprocessing, augmentation, training and testing to be implemented efficiently. The proposed ensemble framework combines the feature extraction power of ResNet50 with several deep learning classifiers so as to obtain a good identification performance at different resolutions and environments. The proposed model performance is tested with the following metrics Accuracy, Precision, Inference Time, and Resolution quality and compared with MobileNetV2, YOLOv8 and the baseline CNN models. Experimental results show that the proposed ensemble ResNet50 framework achieves an accuracy of 98.7% and precision of 98.3%, which is about 6.4%, 4.8%, and 9.2% higher than that of MobileNetV2, YOLOv8, and CNN respectively. Moreover, the proposed method achieves high resolution disease localization capability with an inference time improvement of almost 18% compared with YOLOv8. The proposed system greatly improves the detection accuracy of the early stage and the calculation speed of the system, which is very suitable for smart agriculture applications and real-time monitoring of the health status of crops.
Purpose: The proposed study will develop an AI-assisted automated crop disease detection system for tomato, cotton, and wheat plants based on CNN, SVM, VGG19, and ResNet models, combined with an intelligent treatment recommendation system that will help farmers address diseases in a timely and effective manner.
Design / Methodology / Approach: One of the proposed frameworks uses data pre-processing, augmentation, and comparative training of CNN, SVM, VGG19, and ResNet models, with hyperparameters, dropout regularisation, and a smart prescription module that provides organic, chemical, and agronomic treatment recommendations based on Kaggle public databases.
Research Limitation: The system is restricted to three crop types on class-imbalanced data and has yet to be tested in practical, real-time mobile or field settings, with ResNet showing unstable classification and requiring further improvement.
Findings: CNN had the highest accuracy of 92.38%, followed by VGG19 at 91.40%, SVM at 84.19%, and ResNet at 80.00%. Data augmentation increased overall generalisation by about 5, and hyperparameter optimisation is significant to the overall performance of the model.
Practical Implication: The framework offers a scalable, end-to-end disease detection and treatment advisor framework which can be deployed through mobile applications and edge AI devices to facilitate real-time, offline crop diagnostics for resource-constrained farmers in agricultural settings.
Social Implication: By enabling text-to-speech accessibility and early disease detection with AI, the system will assist low-literacy smallholder farmers, enhance food security, and encourage sustainable farming practices through the targeted and limited use of chemicals.
Originality/Value: The study introduces a unique multi-model benchmarking infrastructure, which includes both automated disease classification and a prescriptive treatment engine and an inclusive text-to-speech interface, showing that shallow CNN networks can be more accurate than deeper ones and creating a pipeline of precision agriculture, spending more responsibly and inclusively on a course-to-course basis.
C. Tripathi, V. Taksande, U. Patel et al.· African Journal Of Applied R...· 0 citations
It is suggested that in order to be implementable in the field, future intelligent agricultural diagnosis systems must be able to balance predictive accuracy, explainability, computational efficiency and field adaptability.
Usman Haruna· Research Journal of Pure Sci...· 0 citations
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