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3 articles for “plant sickness detection”
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Plant Leaf Sickness Recognition Using Image Processing
Abstract: This paper demonstrates the use of image processing techniques in detection of diseases in plants. The system which we have demonstrated in this technique is a software result for computerized detection and computing of the texture enumeration for the leaf diseases in plants. This processing system involves four major steps: (1) in this step we take an RGB image as input and it’s colour transformation structure is created, (2) in …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 3, Issue 2, 2016 · pp. 26–31 Read article
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Identification of Potato Plant Leaf Disease Using Different Artificial Intelligence (AI) Techniques: A Review
Abstract: One of the nation's most vital industries is agriculture. Employment possibilities are abundant in emerging nations like India. The livelihoods of nearly 70% of the global population depend on agriculture. In this study, we have evaluatednumerous articles that have been released on potato plant leaf disease (PLD)diagnosis and classification utilising machine learning (ML)or Deep Learning (DL)methods. There are several causes of leaf diseases in plants, including bacteria, viruses, fungus, and …
Published in Journal of Open Source Developments · Vol. 10, Issue 3, 2023 · pp. 7–19 Read article
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Automated Brinjal Leaf Disease Detection Using Machine Learning: A Sri Lankan Study
Abstract: The spread of plant diseases has a significant negative impact on agricultural output quality and yield, resulting in significant economic losses. Plant disease automation in agricultural science is our country's main concern due to the economic crisis and the emphasis on organic farming practices. Furthermore, the accuracy and efficiency of recognizing sick leaves have increased due to the expanding usage of smart technology. This study uses machine learning and image …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 9, Issue 3, 2022 · pp. 41–56 Read article