Corn Crop Yield Prediction using Deep Learning

Accurate crop yield prediction has emerged as a critical area of research, especially in light of global food security challenges and the need for sustainable agricultural practices. In this study, we focus on predicting the yield of US corn, a staple crop with significant economic and nutritional value. By leveraging data mining techniques, we extract meaningful patterns from large agricultural datasets, aiming to enhance our understanding of the factors influencing crop yields. Accurate predictions can guide farmers and policymakers in making informed decisions, such as optimizing planting schedules, managing resources more effectively, and anticipating potential shortages. In this study, we utilized a variety of machine learning models, such as Logistic Regression, Decision Trees, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Random Forest, Support Vector Machines (SVM), and Long Short Term Memory (LSTM) networks. These models were selected to explore their varying capabilities in handling complex agricultural data and providing accurate predictions. The final outcome, Linear Regression, achieved an impressive accuracy of 93%, demonstrating its effectiveness in predicting crop yields based on the given dataset.