Advancements in Machine Learning for Early Detection of Plant Diseases

Gayatri, Rahangdale and Supesh, Falke and Gauri, Bharti and Shweta, Dewalkar and Prof. Anupam, Chaube and Prof. Rina, Shirpurkar (2024) Advancements in Machine Learning for Early Detection of Plant Diseases. International Journal of Trend in Scientific Research and Development, 8 (5). pp. 558-566. ISSN 2456-6470

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Abstract

Plant detection is a critical task in agricultural automation and environmental monitoring. It involves identifying and classifying plant species, diseases, or other relevant plant characteristics using various techniques, such as image processing, machine learning, and remote sensing. Advances in computer vision and artificial intelligence have enabled the development of robust plant detection systems capable of analyzing vast amounts of data in real-time. These systems can be employed for applications such as precision agriculture, where accurate plant detection can optimize crop management, increase yield, and reduce resource use. This abstract summarizes the current methodologies, challenges, and potential future directions in the field of plant detection, emphasizing the importance of integrating multi-modal data and enhancing the adaptability of detection algorithms to various environmental conditions. One of the essential components of human civilization is agriculture. It helps the economy in addition to supplying food. Plant leaves or crops are vulnerable to different diseases during agricultural cultivation. The diseases halt the growth of their respective species. Early and precise detection and classification of the diseases may reduce the chance of additional damage to the plants. The detection and classification of these diseases have become serious problems. Farmers’ typical way of predicting and classifying plant leaf diseases can be boring and erroneous.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	23 Oct 2024 09:32
Last Modified:	23 Oct 2024 09:32
URI:	http://eprints.umsida.ac.id/id/eprint/14231

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