Farmulate: Optimizing Crop Rotation and Companion Planting Through XGBoost and YOLOv8n Algorithms Based on Soil Analysis

Description

This study addresses the lack of simple and accessible tools for soil analysis, crop rotation, and companion planting, especially for small-scale and beginner farmers. Titled “Farmulate: Optimizing Crop Rotation and Companion Planting Through Machine Learning and Image Processing Algorithms Based on Soil Analysis,” it aims to help users choose suitable crops and planting strategies using machine learning and image processing. Designed for farmers, agricultural practitioners, and learners, the system analyzes soil images and sensor-based nutrient data to identify soil type and recommend appropriate crops and companion plants. It is expected to improve soil health, increase crop yield, and reduce the need for expensive laboratory soil testing, contributing to more sustainable and data-driven farming practices.