Integrated Prediction Model for Crop Yield and Harvesting Using Machine Learning and IoT

Agriculture, constituting 17% of India's GDP and employing 58% of the population, faces productivity challenges without comprehensive knowledge of soil, crops, and weather. To enhance predictability, machine learning techniques integrate historical data with live inputs from sensors and IoT devices. This study employs diverse classifiers—SVM, RF, Linear Regression, Naive Bayes, and Decision Trees—leveraging their unique strengths. Remarkably, the Random Forest classifier emerges as a potent tool for forecasting agricultural yields. The research underscores the efficacy of Random Forest and Naive Bayes in analyzing crops within prevailing climatic conditions, boasting high accuracy in data analysis. This advanced predictive model demands a systematic examination of vast datasets encompassing variables like soil quality, pH levels, and climatic patterns. By synthesizing information from these diverse sources, the model excels in forecasting harvests well in advance. This technical approach not only enhances agricultural productivity but also illustrates the transformative power of machine learning in optimizing crop management strategies for sustainable and resilient farming practices.