A Comprehensive Review of Disease Detection Techniques for Tomato Leaves DOI Open Access

Divyabhavani Ganpisetty,

Navyashree Ganpisetty,

K B Bindushree

и другие.

International Journal of Advanced Research in Science Communication and Technology, Год журнала: 2024, Номер unknown, С. 263 - 272

Опубликована: Дек. 16, 2024

Tomato plants plays vital role in global agriculture, significantly impacting food security and economic stability. However, diseases affecting tomato leaves present substantial challenges to crop yields quality, highlighting the need for effective detection methods. This paper presents a comprehensive review of disease techniques leaves, emphasizing transformative impact advancements image processing, machine learning, deep learning. Approaches are categorized based on their methodologies, including traditional cutting-edge learning frameworks. Key concepts such as segmentation, feature extraction, transfer defined provide foundational understanding. The also identifies critical research gaps, particularly concerning generalizability solutions real-world conditions necessity computational efficiency field applications. Organized by method categories, evaluation metrics, dataset utilization, this encompasses recent up 2024, focusing improving accuracy, scalability, practical implementation. Ultimately, work aims serve an insightful reference researchers practitioners, facilitating advancement systems deployment

Язык: Английский

A Comprehensive Review of Disease Detection Techniques for Tomato Leaves DOI Open Access

Divyabhavani Ganpisetty,

Navyashree Ganpisetty,

K B Bindushree

и другие.

International Journal of Advanced Research in Science Communication and Technology, Год журнала: 2024, Номер unknown, С. 263 - 272

Опубликована: Дек. 16, 2024

Tomato plants plays vital role in global agriculture, significantly impacting food security and economic stability. However, diseases affecting tomato leaves present substantial challenges to crop yields quality, highlighting the need for effective detection methods. This paper presents a comprehensive review of disease techniques leaves, emphasizing transformative impact advancements image processing, machine learning, deep learning. Approaches are categorized based on their methodologies, including traditional cutting-edge learning frameworks. Key concepts such as segmentation, feature extraction, transfer defined provide foundational understanding. The also identifies critical research gaps, particularly concerning generalizability solutions real-world conditions necessity computational efficiency field applications. Organized by method categories, evaluation metrics, dataset utilization, this encompasses recent up 2024, focusing improving accuracy, scalability, practical implementation. Ultimately, work aims serve an insightful reference researchers practitioners, facilitating advancement systems deployment

Язык: Английский

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