Plants Classification Using Machine Learning Algorithm

Water is the principle content in a plant. Along these lines, the development of plants are enormously reliant on the adjustments in plant water content. To advance plant development in water short and dry spell pressure conditions, numerous methods have been considered. The target of this paper is to structure and build up an astute framework, in light of Support Vector Machine (SVM) and machine vision that would advance plant development in a restricted water condition. At long last shading, morphological and textural highlights were separated from a lot of turf grass, wheat, rice plant pictures under dry season pressure conditions. At that point an information arrangement process was overseen utilizing an SVM and ANN. Results indicated that the general arrangement exactness of ANN was 92% and higher correct nesses were acquired when the SVM was utilized as the classifier with a general precision of 98.00% for plant condition (Fresh and Wilted).