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Advancements in Machine Learning for Early Detection of Plant Diseases

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Advancements in Machine Learning for Early Detection of Plant Diseases


Gayatri Rahangdale | Supesh Falke | Gauri Bharti | Shweta Dewalkar | Prof. Anupam Chaube | Prof. Rina Shirpurkar



Gayatri Rahangdale | Supesh Falke | Gauri Bharti | Shweta Dewalkar | Prof. Anupam Chaube | Prof. Rina Shirpurkar "Advancements in Machine Learning for Early Detection of Plant Diseases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-5, October 2024, pp.558-566, URL: https://www.ijtsrd.com/papers/ijtsrd69417.pdf

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.

Digital image processing, Foreground detection, Machine learning, Plant disease detection


IJTSRD69417
Volume-8 | Issue-5, October 2024
558-566
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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