Advancements in rice disease detection through convolutional neural networks: A comprehensive review

Heliyon, Год журнала: 2024, Номер 10(12), С. e33328 - e33328

Опубликована: Июнь 1, 2024

This review paper addresses the critical need for advanced rice disease detection methods by integrating artificial intelligence, specifically convolutional neural networks (CNNs). Rice, being a staple food large part of global population, is susceptible to various diseases that threaten security and agricultural sustainability. research significant as it leverages technological advancements tackle these challenges effectively. Drawing upon diverse datasets collected across regions including India, Bangladesh, Türkiye, China, Pakistan, this offers comprehensive analysis efforts in using CNNs. While some are universally prevalent, many vary significantly growing region due differences climate, soil conditions, practices. The primary objective explore application AI, particularly CNNs, precise early identification diseases. literature includes detailed examination data sources, datasets, preprocessing strategies, shedding light on geographic distribution collection profiles contributing researchers. Additionally, synthesizes information algorithms models employed detection, highlighting their effectiveness addressing complexities. thoroughly evaluates hyperparameter optimization techniques impact model performance, emphasizing importance fine-tuning optimal results. Performance metrics such accuracy, precision, recall, F1 score rigorously analyzed assess effectiveness. Furthermore, discussion section critically examines associated with current methodologies, identifies opportunities improvement, outlines future directions at intersection machine learning detection. review, analyzing total 121 papers, underscores significance ongoing interdisciplinary meet evolving technology needs enhance security.

Язык: Английский

---

A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

Potato Research, Год журнала: 2024, Номер unknown

Опубликована: Авг. 26, 2024

Abstract This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and efficacy prominent algorithms like ResNet, VGG, MobileNet variants classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random genetic algorithms, Bayesian examined, impact on model performance is assessed. Challenges including dataset scarcity, variability symptoms, generalization models across diverse environmental conditions addressed discussion section. Opportunities advancing CNN-based detection, integration multi-spectral imaging remote sensing data, implementation federated learning collaborative training, explored. Future directions propose research into robust transfer deployment CNNs real-time monitoring systems proactive management agriculture. Current knowledge consolidated, gaps identified, avenues future strategies to sustain farming effectively proposed by this review. study paves way advancements AI-driven potentially revolutionizing practices enhancing food security. Also, it aims guide development efforts leading improved crop practices, increased yields, enhanced

Язык: Английский

---

Advancements in maize disease detection: A comprehensive review of convolutional neural networks

Computers in Biology and Medicine, Год журнала: 2024, Номер 183, С. 109222 - 109222

Опубликована: Окт. 10, 2024

Язык: Английский

---