Suggestion of Appropriate Crops Based on Rainfall and Underground Water Analysis

Providing suggestions for appropriate crops can be extremely important in today’s world, as it provides increased agricultural productivity, climate change adaptation, economic development, and sustainable agriculture. Our main motivation is to make recommendations for crop selection based on rainfall and groundwater levels. Water availability is critical for crop growth, and understanding the relationship between rainfall and groundwater levels can help farmers make informed crop selection decisions. This study will identify crops suitable for the local climatic conditions by analyzing historical data on rainfall and groundwater levels in a given region. The study will also take into account soil type and nutrient content when recommending crops. The study’s findings will help farmers, agricultural researchers, and policymakers make crop management and water resource management decisions.This paper aims to use machine learning algorithms and ideologies to successfully implement a website that can inform farmers via a message on their phones about the appropriate crops that can be harvested based on the analysis of rainfall and underground water for that particular district.