Janani Malaisamy*, Jebakumar Rethnaraj
Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1845-1862, 2023, DOI:10.32604/iasc.2023.034280
Abstract Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative yield. Distributing fertiliser in optimum amounts will protect the environment’s condition and human health risks. Early identification also prevents the disease’s occurrence in groundnut crops. A convolutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitrogen nutrient deficiency through image features. Since chlorophyll and nitrogen are proportionate to one another, the Smart Nutrient Deficiency… More >
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