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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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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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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Evaluation of an Artificial Intelligence system for bull sperm morphology evaluation

Theriogenology, Год журнала: 2025, Номер 245, С. 117504 - 117504

Опубликована: Май 29, 2025

The morphological characteristics of bull spermatozoa are usually evaluated visually using bright-field microscopy according to the guidelines proposed by Society for Theriogenology (SFT) Bull Breeding Soundness Evaluation (BBSE). However, analysis is labor consuming and requires experienced personnel obtain reliable results. Nevertheless, artificial insemination industry increasingly demands implementation genomic selection schemes young bulls. Hence, there a growing need more standardized technique analyze semen quality, particularly evaluation abnormalities that affect freezing suitability fertilizing capacity. Therefore, an Artificial Intelligence (AI) algorithm automated classification microscope-acquired images was developed neural networks, specifically YOLO based on convolutional networks (CNNs) were able learn extract relevant features from complex visual data through image segmentation. aim assess ability identify sperm cells in images, establish their viability classify morphology simplified scheme which included only normal or major/minor defect categories. dataset comprised 8243 labeled annotated with bounding boxes allow segmentation learn. performance obtained showed accuracy 82 %, although it not observed all classes (excluding probable case overfitting where reached 100 %), precision 85 % correct morphology. Results thereby confirmed potential applicability without excluding its future achieving optimal performance.

Язык: Английский

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Deep Convolutional Neural Networks for Plant Disease Detection: A Mobile Application Approach (Agri Bot)

International Journal of Intelligent Systems, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Plant diseases imperil global food security, decimating crop yields and endangering farmers’ livelihoods. Rapid, accurate detection remains a challenge, particularly in resource‐constrained environments lacking portable tools. Our contribution, Agri Bot, introduces pioneering deep convolutional neural network (CNN) model, uniquely optimized for mobile deployment, transforming plant disease diagnosis. This novel model integrates lightweight architecture with advanced feature extraction, achieving an exceptional 97.30% accuracy 98.76% area under the curve (AUC). Unlike computationally intensive traditional CNNs, Bot’s innovative design—featuring hybrid autoencoder, max pooling, dropout layers—ensures high‐speed, real‐time performance on devices. Comparative studies reveal superiority, surpassing state‐of‐the‐art models like VGG16 (71.48%) ResNet50 (96.46%), while rivaling InceptionV3 (99.07%) significantly lower computational demands. By delivering precise, accessible diagnostics to remote regions, Bot revolutionizes agricultural management, enhancing resilience security.

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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