Abstract
Today Potato becomes most well-known crops in world. Now Plant crop disease detection has transferred as an operative research domain. As per enhancement of requirements of methods and demands for detection of diseases of crops are crucial part of agriculture. Many disease affects the perfect enhancement of plants of potatoes. Some Observable problems are very much visible in potato plants leaf areas of affected regions As Early (EB) and Late (LB) Blight. Particularly, image based approach offers the way of gathering knowledge regarding plants for quantitative analysis. In case of other side, manual detection of crop diseases needs more work effort, expert domain persons, execution time higher. Therefore, integration of image processing and machine learning is required to enable the diagnosis of leaf images with disease. CNN is used for image Detection and Analysis of potato diseases and gives the best result than other classifier. Here some classifiers are used for this research paper such as SVM, Random Forest, Logistic Regression & Sequential model. In this proposed work, the model validation, training is done using CNN to identifying and extraction of necessaryinformation of used datasets and for determining that leaf are affected or not. This model achieved accuracy of 97.92% that indicates the suitable outcomes for identifying the crop diseases.
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Dataset Used. https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset
Acknowledgement
We would like to express our very much appreciation to Mr. Pramit Brata Chanda faculty of department of Computer Science and Engg. Of Kalyani Govt. Engg. College and also the other faculty members of department for their suggestions during this research study on plant diseases. His willingness to provide us valuable time that is very much appreciated.
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Paria, A., Roy, S., Chanda, P.B., Jha, D.K. (2024). Identification and Multi-classification of Several Potato Plant Leave Diseases Using Deep Learning. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_22
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