Potato Plant Leaves Disease Detection and Classification using Machine Learning Methodologies

• Published in: IOP conference series. Materials Science and Engineering Vol. 1022; no. 1; pp. 12121 - 12129
• Main Authors: Singh, Aditi, Kaur, Harjeet
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.01.2021
• Subjects: Agriculture; Classification; Digitization; Feature extraction; Image Processing; Image segmentation; Kmean; Machine learning; Methodology; Plant disease detection using image classification; Potatoes; Support vector machines; SVM
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/1022/1/012121

Agriculture is one of the essential sectors for the survival of humankind. At the same time, digitalization touching across all the fields that became easier to handle various difficult tasks. Adapting technology as well as digitalization is very crucial for the field of agriculture to benefit the farmer as well as the consumer. Due to adopting technology and regular monitoring, one can able to identify the diseases at the very initial stages and those can be eradicated to obtain a better yield of the crop. In this document, a methodology was proposed for the detection as well as the classification of diseases that occur for the potato plants. For this scenario, the openly accessible, standard, and reliable data set was considered which was popularly known as Plant Village Dataset. For the process of image segmentation, the K-means methodology was considered, for the feature extraction purpose, the gray level co-occurrence matrix concept was utilized, and for the classification purpose, the multi-class support vector machine methodology was utilized. The proposed methodology able to attain an accuracy of 95.99%.