Deep learning-based mobile application for nutrient deficiency identification of rice plants using ADAM Optimizer

The second-largest rice-produced country after China is India. Nowadays, rice plants are affected by nutrient deficiencies. Rice plants require adequate amounts of three critical nutrients: nitrogen (N), phosphorus (P), and potassium (K). The main purpose is to detect the NPK nutrient deficiency and recommend the fertilizer for corresponding nutrient deficiency so that it can help farmers to increase their crop yield. For image classification, a deep learning model such as a Convolutional Neural Network is employed, and the model is optimized and trained using the Adam Optimizer. In the proposed model SoftMax Activation function has been used to detect nutrient deficiency with an overall accuracy of 98.75% and an android based mobile application is developed for easy usage to the farmers.