Identification of Nutritional Deficiencies in Crops Using Machine Learning and Image Processing Techniques

Image Processing (IP) and Machine Learning (ML) are used to identify nutritional deficiencies in crops. Crops require an appropriate quantity of vitamins and minerals to finish and maintain a balanced lifetime. A adequate number of six key vitamins and minerals, such as nitrogen, calcium, phosphorus, potash, sulphur, and magnesium (mg), are highly critical for regular and robust crop development. Nutritional deficiencies or deficiency causes difficulty in performing out everyday crop operations and, as a result, reduces production. As a result, having a rapid assessment for nutritional intake is critical. Crops frequently have a noticeable shortage on their leaflets, with distinct configurations for every ingredient. The goal of our planned effort is to create an autonomous and dependable inexpensive alternative for nutritional deficit detection. The datasets for insufficient and healthier branches are constructed utilizing IP techniques such as RGB colour feature extractor, real-time texture recognition, edge recognition, and so on. This produced database will be used as a training images for supervised ML to discover and identify specific nutritional deficiencies and healthier seedlings in order to take precautionary actions to optimize production.