Detection of Micro and Macro Nutrient Deficiency in Okra (Abelmoschus Esculentus L) Plant Leaves Using Machine Learning Approach

Nutrient deficiency symptoms often appear on plant leaf. However, from naked eye visibility is not precisely correct. Traditional methods of nutrient assessment are often time-consuming. This study focuses on an automated system that utilizes machine learning and image processing to identify the nutrient deficiencies in Okra leaves accurately. Features from the leaf image such as color are extracted, and the proposed model is trained to recognize specific nutrient deficiency. This study aims to help agriculture experts, consultants, and farmers take more accurate measurements and make better decisions. It will also help with early detection of problems.