Enhancement of Crop Yield Prediction using an Optimized Deep Network

Abstract

The importance of predicting crop yields lies in ensuring food security and optimizing agricultural practices. Precise crop yield forecasts empower farmers and policymakers to make well-informed decisions regarding harvesting, planting, and resource allocation, ultimately affecting the availability and affordability of food. While various methods for predicting crop yields exist, they often fall short in accuracy and efficiency. This research introduces the Honey Badger-based Deep Neural Predictive Framework (HBbDNPF). The model combines the concept of Honey Badger optimization and deep neural network to effectively predict different crop yields. The method includes modules such as preprocessing, feature extraction, and prediction. The module reduces the complexity and enhances the accuracy of the crop yield classification. The method is tested with the Unmanned Arial Vehicle (UAV) spectral image dataset. The model significance improved the accuracy of the prediction and consumed less time due to the selected features. The model validated the accuracy of 99.9% with 99.7% precision and 99.5% recall rate. By harnessing the synergy of optimization and deep learning, HBbDNPF empowers informed agricultural decision-making, resource allocation, and food production efficiency, contributing to global food security.