Crop Care Tech: A Web Based Application for Crop, Fertilizer Recommendation, Disease Detection by Using ML, DL

Abstract

Agriculture is the Backbone of the India’s economy. Agriculture is the foundation of human civilization, embodying the art and science of cultivating crops and rearing livestock for sustenance and economic prosperity. Due to the poor weather, climate conditions, lack of proper care there are huge losses to the farmers in terms of their productivity. Because of not much knowledge, education in the rural area farmers tend to commit mistakes by not giving their crop proper treatment like good fertilizers to grow the crop, taking care of the disease which has been caught up by the crop. This study aims to develop a website utilizing machine learning models for crop recommendations, taking into account inputs such as pH values, temperature, and soil nutrients like nitrogen, potassium, phosphorus etc. Various machine learning algorithms including logistic regression, linear regression, support vector machines, random forests, naive bayes, xgboost, decision trees are used, with random forests and xgboost gives the higher prediction results.  So basically, we are expecting that after taking valuable inputs from user like soil nutrients, images of the crop we will predict the desired output with highest efficiency. The main objective of this project is building a website which will help farmers to make the effective cultivation by providing good information like which crop is suitable for their soil, and which fertilizers are suitable to grow. Ultimately, these systems serve as a crucial tool in advancing modern agriculture, leveraging technology and data analysis to assist farmers in making decisions that contribute to food security and agricultural sustainability. Accessible through user-friendly platforms, crop recommendation systems empower farmers to harness the benefits of technology and data, thereby enhancing agricultural productivity, financial gains, and global food security through these modern agricultural advancements.