Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review

Remote Sensing, Год журнала: 2025, Номер 17(4), С. 698 - 698

Опубликована: Фев. 18, 2025

Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, require specialized skills resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution accurate timely identification pests, thereby reducing crop losses optimizing resource allocation. By leveraging its advantages in image processing, technology has significantly enhanced accuracy disease pest detection identification. This review provides comprehensive overview recent advancements applying algorithms detection. It begins by outlining limitations traditional this domain, followed systematic discussion latest developments various techniques—including classification, object detection, semantic segmentation, change detection—to Additionally, study highlights role large-scale pre-trained models transfer improving scalability across diverse types environmental conditions. Key such enhancing model generalization, addressing small lesion ensuring availability high-quality, training datasets, critically examined. Emerging opportunities monitoring through advanced also emphasized. Deep technology, with powerful capabilities data processing pattern recognition, become pivotal tool promoting sustainable practices, productivity, advancing precision agriculture.

Язык: Английский

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Artificial Intelligence-Assisted Breeding for Plant Disease Resistance

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(11), С. 5324 - 5324

Опубликована: Июнь 1, 2025

Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language large multi-modal model), has emerged as transformative tool enhance detection omics prediction science. This paper provides comprehensive review of AI-driven advancements detection, highlighting convolutional neural networks their linked methods through bibliometric analysis from recent research. We further discuss the groundbreaking potential models interpreting complex patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic phenomic selection by enabling high-throughput resistance-associated traits, explore AI’s role harmonizing multi-omics data predict disease-resistant phenotypes. Finally, propose some challenges future directions terms data, model, privacy facets. also provide our perspectives on integrating federated with for prediction. guide into breeding programs, facilitating translation computational advances crop

Язык: Английский

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Analysing the Performance and Interpretability of CNN-Based Architectures for Plant Nutrient Deficiency Identification

Computation, Год журнала: 2024, Номер 12(6), С. 113 - 113

Опубликована: Июнь 3, 2024

Early detection of plant nutrient deficiency is crucial for agricultural productivity. This study investigated the performance and interpretability Convolutional Neural Networks (CNNs) this task. Using rice banana datasets, we compared three CNN architectures (CNN, VGG-16, Inception-V3). Inception-V3 achieved highest accuracy (93% banana), but simpler models such as VGG-16 might be easier to understand. To address trade-off, employed Explainable AI (XAI) techniques (SHAP Grad-CAM) gain insights into model decision-making. emphasises importance both in demonstrates value XAI building trust these models.

Язык: Английский

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Early-stage Cardiomegaly Detection and Classification from X-ray Images Using Convolutional Neural Networks and Transfer Learning

Intelligent Systems with Applications, Год журнала: 2024, Номер unknown, С. 200453 - 200453

Опубликована: Окт. 1, 2024

Язык: Английский

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Research on Tea Disease Model Based on Improved ResNet34 and Transfer Learning

Опубликована: Апрель 12, 2024

Язык: Английский

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