ENHANCED DEEP LEARNING BASED PRECISION AGRICULTURE: A DECISION SUPPORT SYSTEM FOR ENHANCING CROP RECOMMENDATION ACCURACY USING CONVOLUTIONAL NEURAL NETWORKS (CNN)

Abstract

Machine learning-based crop recommendation models are invaluable tools for enhancing modern AI-based farming, assisting in decisions about the selection of crops to optimize yield performance and growth. This research introduces an intelligent strategy and explainable artificial intelligence (XAI) principles based on the Convolutional Neural Network (CNN) method due to the growing demand for interpretability in modern farming decision-making, Utilizing the "Smart Agricultural Production Optimizing Engine” dataset procured from Kaggle. The proposed CNN model gives remarkable results through a comprehensive examination of soil and environmental boundaries like Nitrogen (N), Phosphorus (P), Potassium (K) levels, temperature, moistness, pH, and precipitation. Our results illustrate that the proposed framework essentially moves forward the precision of trim suggestions, advertising a promising arrangement for modernizing agricultural practices and guaranteeing maintainable crop yields.

Keywords

• accuracy rates
• agricultural parameters
• convolutional neural network (cnn)
• crop recommendation systems
• precision agriculture