Optimizing Crop Yield through A Data-Driven Approach with IoT Data Analytics and Machine Learning Algorithms

Agriculture has long been the backbone of the Indian economy, defining the country’s social and cultural milieu. Some of the most common difficulties that farmers face includes selecting suitable crops for their area and utilizing the necessary fertilizers, which can lead to a drop in production. To address these challenges, precision agriculture is employed. Precision agriculture is a smart and modern farming methodology that uses IoT-based research to provide information on soil qualities, types of soil, and crop production statistics. This assists farmers in choosing the optimum crops to plant and provides fertilizer recommendations based on specific location characteristics, also predicting the likelihood of plant diseases. In this project, we introduce a recommendation system by applying machine learning models such as Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Naïve Bayes on a crop dataset, which is entirely IoT-based. The system recommends the best crop for the location’s particular parameters with the highest accuracy. The system achieved an accuracy of 99% for crop recommendation and 92% for plant disease prediction. This research utilizes soil nutrients and pH data as inputs to create a website that predicts the most suitable crops for a particular soil type and suggests appropriate fertilizers. Additionally, the system predicts plant diseases and provides information about the diseases, including suggested cures. Thousands of farmers will be able to access the system through a Flask-based web interface.