Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning

Abstract

The optimization of crop yield projections has arisen as a major problem in modern agriculture, due to the increasing demand for food supply and the necessity for effective resource management. Precision and scalability are hampered by the limits associated with conventional agricultural production prediction techniques, which mostly rely on observations and simple data sources. While methods like random forest (RF) and K-nearest neighbors (KNN) are widely used, their reliance on personal assessments and insufficient knowledge of crop attributes typically results in less accurate forecasts and makes them unsuitable for agricultural precision. The suggested method combines deep learning, spectral unmixing, and hyperspectral imaging methods to overcome these obstacles. With the use of hyperspectral imaging, which records a vast array of data that is not visible to the human eye, crop attributes may be thoroughly examined and can identify the unique spectral fingerprints of different agricultural constituents by using spectral unmixing approaches, which makes it easier to evaluate the health and growth phases of the crop. Then, using this augmented spectral data, deep learning algorithms create a solid, data-driven basis for precise crop production prediction. MATLAB has been used in the suggested workflow. The combination of deep learning, spectrum unmixing, and hyperspectral imaging provides a comprehensive, cutting-edge approach that goes beyond the constraints of conventional techniques were implemented in python. Some of the algorithms that were examined, this one with integration has the lowest Root Mean Square Error (RMSE) of 0.15 and Mean Absolute Error (MAE) of 0.14, demonstrating higher prediction accuracy above other current models. This novel method represents a substantial breakthrough in precision agriculture while also improving crop production prediction.