A Climate-Resilient Agriculture Framework Using Machine Learning and Web-Based Decision Support Systems

This result suggests the new model helps farms adapt to climate shifts by cutting down on crop dangers, boosting planning precision, one step at a time. This system provides practical benefits and recommends through a user-friendly interface accessible on low-bandwidth networks. Crop disease detection, weather-based crop recommendation, and market trend analysis, along with a multilingual chatbot for farmer assistance is integrated by machine learning models in this proposed system. Plant diseases from leaf images are identified by using Convolutional Neural Networks, while regression and time-series models assist in climate and market analysis. Agricultural production face challenges in farming due to climate change, irregular rainfall, rising temperatures, frequent pest outbreaks, and changing market conditions. This paper clearly gives the insights about the web-based climate-resilient agriculture system designed to support informed decision-making using data-driven techniques.