Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Aim:

         To develop a robust and accurate crop yield prediction system, crop yield statistics, leveraging advanced machine learning techniques to promote sustainable agricultural practices and enhance global food security.

Abstract:

       Agriculture contributes a significant amount to the economy of India due to the dependence on human beings for their survival. The main obstacle to food security is population expansion leading to rising demand for food. Farmers must produce more on the same land to boost the supply. Through crop yield prediction, technology can assist farmers in producing more. This paper’s primary goal is to predict crop yield utilizing the variables of rainfall, crop, meteorological conditions, area, production, and yield that have posed a serious threat to the long-term viability of agriculture. Crop yield prediction is a decision-support tool that uses machine learning and deep learning that can be used to make decisions about which crops to produce and what to do in the crop’s growing season. It can decide which crops to produce and what to do in the crop’s growing season. Regardless of the distracting environment, machine learning and deep learning algorithms are utilized in crop selection to reduce agricultural yield output losses

Proposed System:

        The proposed system aims to improve crop yield prediction by collecting historical crop yield information. This data will be preprocessed to ensure its quality and consistency. Subsequently, machine learning algorithms such as KNeighbors Regressor, Random Forest Regressor, Decision Tree Regressor, and Bagging Regressor will be applied to model crop yield predictions. Hyperparameter tuning will be performed using GridSearchCV to optimize model performance. Finally, a web application will be developed using Flask, Python, HTML, CSS, Bootstrap, and JavaScript. The application will feature user login and registration interfaces along with a prediction interface, allowing users to easily interact with the system and obtain crop yield predictions based on the collected data.

Advantage:

• By employing various machine learning algorithms and fine-tuning them using GridSearchCV, the system aims to provide reliable and accurate crop yield predictions, assisting in better decision-making for farmers and agricultural planners.
• The web application provides an intuitive and easy-to-use interface for users, with login, registration, and prediction features, enabling farmers, researchers, and agricultural stakeholders to access predictions with minimal effort.