Data-Driven Approaches for Crop Yield Prediction Using Machine Learning Techniques

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Abstract
Sustainable development of agriculture along with food safety and precise resource handling depends on correct crop yield prediction. Traditionally yielded forecast systems find it challenging to handle agricultural data of large scale and multi-source nature thus resulting in inaccurate prediction outcomes. This study develops data-connected crop yield prediction through deep learning framework implementation with TensorFlow and Keras which creates highly precise instant forecast mechanisms. Identifying historical climate data, remote sensing imagery as well as soil characteristics, this approach implements Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for analysis. Through its dynamic learning mechanisms the model detects sophisticated patterns found in agricultural datasets better than both established statistical methodologies and machine learning models at execution speed and adaptability rates. The prediction accuracy through deep learning reached 93% while yield estimate errors declined by 65% and continuous forecasting became 90% faster. The research shows AI predictive analytics can enhance farming choices by improving crop management techniques which benefits agricultural sustainability on a global scale through data science and smart decision systems.

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