Revolutionizing Wheat Farming in Arid Regions With Machine-Learning Enabled Cloud Computing for Yield Simulation

Abstract

This research provides a thorough examination of the application of machine learning enabled cloud computing for revolutionising wheat farming in dry environments. The study aims to address the issues that farmers in these areas confront, such as erratic weather patterns, water shortages, and poor soil quality. The suggested method is creating a model that can estimate crop yields reliably based on previous weather data, soil quality, and other environmental parameters. To accomplish this aim, the study team used machine learning techniques like as neural networks to train the model on a vast collection of historical weather and yield data. The model was subsequently incorporated into a cloud computing platform, enabling for large-scale simulations and the optimisation of crop management tactics to maximise yields. The study's findings show that the recommended technique is effective in increasing agricultural yields while decreasing water use. The simulation findings show that the model can estimate yields with high accuracy, allowing farmers to make educated crop management decisions. Furthermore, the cloud computing platform allows for real-time crop monitoring, allowing for early interventions in the event of unfavourable weather events or other environmental conditions that may effect yields. Overall, the work has important implications for wheat growing in dry locations, where farmers confront unique problems. The proposed method has the potential to boost agricultural yields, minimise water use, and raise farmers' overall profitability. The study's findings may also be applied to other crops cultivated in dry environments, offering a framework for sustainable farming practises in similar settings.