Nutritional Deficit Detection in Crops Using Machine Learning
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Abstract
IP and ML are used to analyze images of crops for signs of nutrient deficiency. Vitamins and minerals are essential to a plant's healthy development and growth. Nitrogen, calcium, phosphorus, potash, sulphur, and magnesium (mg), to name a few, are essential for consistent and vigorous crop growth. Reduced crop output is the direct outcome of nutritional inadequacies, which make it harder to carry out routine agricultural tasks. Therefore, it is essential to have a quick evaluation of food consumption. Many crop leaflets exhibit glaring shortages, with customized layouts for each component. Our planned work is to provide a self-sufficient, trustworthy, low-cost alternative for identifying nutritional deficiency. Datasets for both unhealthy and full-functioning branches are built using IP methods including RGB color feature extractor, real-time texture recognition, edge identification, and so on. The resulting database will serve as training data for supervised ML, which will then be used to spot signs of nutrient deficiency and choose the strongest seedlings for further cultivation.
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