Categorization of Nutritional Deficiencies in Plants With Random Forest

Agriculture supports to greater extent for the means of living in India, especially in rural areas. But generally the crop production attained by farmers would be much below the optimal production. The main reason for the crop production gap is due to the lack of essential soil nutrients and irrigation in the agricultural farms. To escalate the crop production, it is essential to balance the chemical elements or nutrients present in the soil with varying parameters of soil like the pH and soil moisture. Crop productivity can be increased to optimum level by efficient soil nutrient management. In case of Nutrient deficiencies, visual symptoms will appear on the leaf. This paper put forwards a method to identify the nutrient deficiencies of cabbage leaves by making use of visual symptoms appearing on the leaves by Classification. Seven types of deficiencies N, P, K, Ca, B, Zn and Mg are considered in this study. The proposed study consists of creation and pre- processing of a set of images consisting of nutrient deficient and healthy leaves of cabbage, feature extraction and by using Random Forest performing multi class classification of nutrient deficient leaves. The paper focuses on recognizing the visual indications of nutritional deficiency and thereafter classification.