Summary
Assessing nutrition in the soil and correctly predicting crop yields play important roles in precision agriculture, made possible recently by ML, DL and IoT. This analysis examines how soil nitrogen, phosphorus and potassium (NPK) content is measured, with a special focus on wireless and handheld NPK sensors. We also study using attention-based deep learning networks and optimization to recommend crops more effectively. Experimental systems and relevant literature were thoroughly examined, looking at models designed to use data on soil, weather and seasonal aspects. To support crop recommendation and yield estimation, Random Forest, k-NN, SVM, Logistic Regression and DL techniques including GRU, RNN and hybrid structures were all assessed. Researchers also looked at using edge computing, s
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