Design of Crop Classification Model Based on Soil Information
Krish L Sharma, Yashaswi Patel, Ankur Jain · 2024
The adoption of contemporary techniques to boost agricultural output is unavoidable given worries about the growth of the world's population and the scarcity of food. Artificial neural networks (ANN) in particular have attracted interest recently for the analysis of agricultural data so that farmers may maximise resource allocation, reduce waste, and improve overall efficiency by accurately identifying their crops, giving farmers useful information for making data-driven crop production decisions. ANNs are capable of learning intricate correlations between input characteristics and output labels. The paper proposes an ANN model that classifies crops based on a variety of soil properties. 22 different crops are included in the data collection. The study provides a comparative study of different ANN models with respect to activation function and loss function yielding 8 different models. The proposed model correctly detects crop types using specific soil parameters. This study aids in the creation of simple farming solutions that facilitate decision-making and raise overall agricultural productivity.