CNN-based Soil Fertility Classification with Fertilizer Prescription

Abstract

Soil fertility plays a vital role in crop growth, and thus, the rapid acquisition of soil fertility levels and applying precise fertilizer is significant for sustainable agricultural development. However, obtaining accurate soil fertility estimates proves difficult due to the traditional practice of laboratory analysis of soil samples. This study proposes fertilizer prescriptions based on the Convolutional Neural Networks (CNNs) classifier results. The soil fertility is classified as HIGH, MEDIUM, or LOW fertile based on the chemical measurements of soil parameters, including EC, pH, OC, P, K, S, Zn, B, Cu, Fe, and Mn. The experiments were carried out by varying kernel size from $3\times 3$ to $7\times 7$ and input grid size from $11\times 11$ to $13\times 13$. The proposed approach outperformed with an Accuracy of 97.24% without oversampling the dataset for kernel size $3\times 3$ and input grid size $11\times 11$. Further, for the dataset oversampled using Synthetic Minority Oversampling (SMOTE) technique, the proposed approach achieved the highest Accuracy of 97.52% for kernel size $3\times 3$ and input grid size $12\times 12$. The study helps in the precise application of fertilizers for specific crops based on classification results. © 2023 IEEE.