Analysis of Recognition Performance of Plant Leaf Diseases Based on Machine Vision Techniques

Imdadul Haque, Mohsin Alim, Mahbub Alam, Samia Nawshin, Sheak Rashed Haider Noori, Md. Tarek Habib


Agriculture is the primary source of income for the majority of the population in Bangladesh. Agriculture is also a big part of the economy of the country. Therefore, it's more necessary to grow our crops and fruits and boost their harvests. Fruits are adored by the people of this country, and farmers love growing fruits. Owing to numerous diseases, both the quality and quantity of fruits are not meeting expectations. Native fruits are contracting many types of new diseases, and the magnitude of the problem is increasing alarmingly. To deal with this issue, quick detection of the disease and correct treatment or recuperation is required. In many cases, locals fail to even detect rare diseases. Thanks to the huge advancement in technology, rare diseases can now be detected with the use of the right technologies. A good plant's growth is dependent on its leaves. Early leaf disease detection can help in keeping the leaves disease-free, as well as the plants and fruits. Our research focuses on identifying litchi leaf diseases by employing sophisticated image processing technologies to ensure the freshness of the leaves. A machine-vision-based technique, i.e., the Convolutional Neural Network (CNN), has been used in this research work.


Doi: 10.28991/HEF-2022-03-01-09

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Agriculture; Litchi; Leaf Disease; Deep Learning; Convolutional Neural Network; Plants; Disease Recognition.


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DOI: 10.28991/HEF-2022-03-01-09


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