CROP YIELD PREDICTION USING MACHINE LEARNING ALGORITHMS

CROP YIELD PREDICTION USING MACHINE LEARNING ALGORITHMS

Shashank C, Prof. Alamma B H

Department of Master of Computer Applicatios

Dayananda Sagar College of Engineering

Bangalore

ABSTRACT:
The Agriculture of predicting crop yield is essential to agricultural planning and decision-making. Farmers can reduce risks related to weather, pests, and diseases, increase production efficiency, and optimize resource allocation with the aid of accurate crop output predictions. Recently, a number of industries, including agriculture, have seen promising results from the use of machine learning algorithms. In this project, methods from machine learning will be used to build a crop yield forecast model. Suggested model incorporates historical data as input features, including weather patterns, soil characteristics, fertilization techniques, and crop management techniques. Artificial neural networks, random forest models, and support vector algorithms are just a few of the machine learning methods that are researched in search of the best- performing model. The dataset gathered for the model's training and testing comes from many agricultural locations, proving its generalizability. Utilizing suitable evaluation The model is evaluated using metrics like median total error, the root mean square erroneous and degree of determination..

Cross-validation methods are also used to verify the robustness of the model and avoid overfitting. To evaluate the generated model's superiority in terms of accuracy and predictive capacity, its performance is contrasted with that of conventional statistical methods. The findings of this study have important ramifications providing key insights for the agricultural community so they may enhance making choices, minimize risk, and better allocate resources. In order to increase output and minimize ecological impact, landowners can take proactive actions including altering the application of fertilizer, watering, and insect management strategies. This abstract concludes by highlighting the potential of machine learning techniques for predicting agricultural yield. The suggested method shows its efficacy in making reliable forecastsenabling producers and crop professionals to maintain sustainable and efficient techniques while making informed decisions to increase total productivity in agriculture.

Keywords: Feature Engineering, Ensemb1e Techniques, Hyperparameter Tuning, Resource Optimization, Crop Yie1d Prediction, Agricu1ture,Machine Learning.