Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture

Artificial Intelligence Review, Год журнала: 2025, Номер 58(3)

Опубликована: Янв. 17, 2025

Abstract Plant diseases cause significant damage to agriculture, leading substantial yield losses and posing a major threat food security. Detection, identification, quantification, diagnosis of plant are crucial parts precision agriculture crop protection. Modernizing improving production efficiency significantly affected by using computer vision technology for disease diagnosis. This is notable its non-destructive nature, speed, real-time responsiveness, precision. Deep learning (DL), recent breakthrough in vision, has become focal point agricultural protection that can minimize the biases manually selecting spot features. study reviews techniques tools used automatic state-of-the-art DL models, trends DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, reference datasets more than 278 research articles were analyzed subsequently highlighted accordance with architecture deep models. Key findings include effectiveness imaging sensors like RGB, multispectral, hyperspectral cameras early detection. Researchers also evaluated various architectures, such as convolutional neural networks, transformers, generative adversarial language foundation Moreover, connects academic practical applications, providing guidance on suitability these models environments. comprehensive review offers valuable insights into current state future directions detection, making it resource researchers, academicians, practitioners agriculture.

Язык: Английский

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DM-YOLO: improved YOLOv9 model for tomato leaf disease detection

Frontiers in Plant Science, Год журнала: 2025, Номер 15

Опубликована: Фев. 11, 2025

In natural environments, tomato leaf disease detection faces many challenges, such as variations in light conditions, overlapping symptoms, tiny size of lesion areas, and occlusion between leaves. Therefore, an improved method, DM-YOLO, based on the YOLOv9 algorithm, is proposed this paper. Specifically, firstly, lightweight dynamic up-sampling DySample incorporated into feature fusion backbone network to enhance ability extract features small lesions suppress interference from background environment; secondly, MPDIoU loss function used learning details margins order improve accuracy localizing margins. The experimental results show that precision (P) model increased by 2.2%, 1.7%, 2.3%, 2%, 2.1%compared with those multiple mainstream models, respectively. When evaluated dataset, was 92.5%, average (AP) mean (mAP) were 95.1% 86.4%, respectively, which 3%, 1.4% higher than P, AP, mAP YOLOv9, baseline model, method had good performance potential, will provide strong support for development smart agriculture control.

Язык: Английский

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Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring

Frontiers in Plant Science, Год журнала: 2025, Номер 15

Опубликована: Янв. 9, 2025

Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional methods rely on manual visual inspection, which is inefficient prone to subjective bias. The application of deep learning in image recognition has led object detection models such as YOLO (You Only Look Once), have shown high efficiency identification. However, complex climatic conditions real environments challenge model robustness, current mainstream struggle with accurate the same diseases across different plant species. This paper proposes SIS-YOLOv8 model, enhances adaptability climates by improving YOLOv8 network structure. research introduces three key modules: 1) a Fusion-Inception Conv module improve feature extraction against backgrounds like rain haze; 2) C2f-SIS incorporating Style Randomization enhance generalization ability crop extract more detailed features; 3) an SPPF-IS boost robustness through fusion. To reduce model's parameter size, this study employs Dep Graph pruning method, significantly decreasing volume 19.9% computational load while maintaining accuracy. Experimental results show outperforms original YOLOv8n tasks potatoes tomatoes, improvements 8.2% accuracy, 4% recall rate, 5.9% mAP50, 6.3% mAP50-95. Through these structure optimizations, demonstrates enhanced environments, offering solution automatic detection. By our approach not only advances but also contributes broader adoption AI-driven solutions sustainable management diverse climates.

Язык: Английский

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Innovative Deep Learning Framework for Accurate Plant Disease Detection and Crop Productivity Enhancement

Cognitive Computation, Год журнала: 2025, Номер 17(1)

Опубликована: Янв. 30, 2025

Язык: Английский

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Exploring Deep Learning Approaches for Detecting Nutritional Deficiencies in Crop Leaves: A Comprehensive Overview

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Detection of Small-Sized Electronics Endangering Facilities Involved in Recycling Processes Using Deep Learning

Applied Sciences, Год журнала: 2025, Номер 15(5), С. 2835 - 2835

Опубликована: Март 6, 2025

In Japan, local governments implore residents to remove the batteries from small-sized electronics before recycling them, but some products still contain lithium-ion batteries. These residual may cause fires, resulting in serious injuries or property damage. Explosive materials such as mobile (such power banks) have been identified fire investigations. Therefore, these fire-causing items should be detected and separated regardless of whether other processes are use. This study focuses on automatic detection using deep learning electronic products. Mobile were chosen first target this approach. study, MATLAB R2024b was applied construct You Only Look Once version 4 algorithm. The model trained enable results show that model’s average precision value reached 0.996. Then, expanded three categories items, including batteries, heated tobacco (electronic cigarettes), smartphones. Furthermore, real-time object videos detector carried out. able detect all accurately. conclusion, technologies significant promise a method for safe high-quality recycling.

Язык: Английский

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Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing

Agriculture, Год журнала: 2025, Номер 15(7), С. 733 - 733

Опубликована: Март 28, 2025

A disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit (Passiflora edulis [Sims]) task. Passiflora edulis, as tropical subtropical tree, loved worldwide for its unique flavor rich nutritional value. The experimental results demonstrate that model performs excellently across various metrics, achieving precision of 0.93, recall 0.88, an accuracy 0.91, mAP@50 (average at IoU threshold 0.50) 0.90, mAP@50–95 thresholds from 0.50 to 0.95) 0.60, F1-score significantly outperforming traditional object models such Faster R-CNN, SSD, YOLO. experiments show offers significant advantages with multi-scale complex backgrounds. This study proposes lightweight deep learning incorporating (SPAM) detection. Built upon Convolutional Neural Network (CNN) backbone, integrates dynamically selective enhance performance cases backgrounds objects. Experimental has superior precision, recall, mean average (mAP) compared state-of-the-art while maintaining computational efficiency.

Язык: Английский

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Artificial intelligence assisted tomato plant monitoring system – An experimental approach based on universal multi-branch general-purpose convolutional neural network

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 224, С. 109201 - 109201

Опубликована: Июнь 27, 2024

Язык: Английский

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Image Processing of Big Data for Plant Diseases of Four Different Plant Categories

International Journal of Computer Vision and Image Processing, Год журнала: 2024, Номер 14(1), С. 1 - 32

Опубликована: Авг. 29, 2024

In this research, plant pathogens are considered as big data because of the numerical counts for high intensity pixels in images. The research presents an automated approach early detection diseases using image processing techniques. By analyzing color features leaf areas, k-means algorithm segmentation and Gray-Level Co-Occurrence Matrix (GLCM) used disease classification. A novelty is that it illustrates four categories plants to analyze compare: (1.) Grain, represented by Rice Plant Leaf Data; (2.) Fruit, banana data, (3.) Flower, sunflower data; (4.) Vegetable, potato data. Six stages applied real smut rice, black sigatoka banana, scars sunflower, late blight potato. Finally, a comparison each types, conclusions, future directions presented.

Язык: Английский

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Deep Learning Applications for Real-Time and Early Detection of Fall Armyworm, African Armyworm, and Maize Stem Borer

Machine Learning with Applications, Год журнала: 2024, Номер unknown, С. 100596 - 100596

Опубликована: Окт. 1, 2024

Язык: Английский

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