Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear. This study was conducted to (1) assess the performance of widely recognized remote sensing indices to predict wheat yield at different growth stages, (2) evaluate the predictive accuracy of different yield predictive machine learning models, (3) determine the appropriate growth period for wheat yield prediction in arid regions, and (4) evaluate the impact of climate parameters on model accuracy. The vegetation indices, widely recognized due to their proven effectiveness, used in this study include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Atmospheric Resistance Vegetation Index (ARVI). Moreover, four machine learning models, viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), and Bagging Trees (BTs), were evaluated to assess their predictive accuracy for wheat yield in the arid region. The whole wheat growth period was divided into three time windows: tillering to grain filling (December 15–March), stem elongation to grain filling (January 15–March), and heading to grain filling (February–March 15). The model was evaluated and developed in the Google Earth Engine (GEE), combining climate and remote sensing data. The results showed that the RF model with ARVI could accurately predict wheat yield at the grain filling and the maturity stages in arid regions with an R² > 0.75 and yield error of less than 10%. The grain filling stage was identified as the optimal prediction window for wheat yield in arid regions. While RF with ARVI delivered the best results, GB with EVI showed slightly lower precision but still outperformed other models. It is concluded that combining multisource data and machine learning models is a promising approach for wheat yield prediction in arid regions.

Digital farming has revolutionized agriculture by integrating technologies like IoT, AI, big data analytics, and remote sensing. This paper explores the impact of digital farming on agricultural engineering practices, highlighting the changes it has brought to the agri-food landscape. By using real-time data collection, analysis, and predictive modeling, agricultural engineers can make informed decisions, enabling precise and sustainable resource management. Precision agriculture technologies can reduce fertilizer and pesticide use by up to 30%, increase yields by 10–20%, and conserve water by up to 50%. Digital farming practices have also increased efficiency and productivity, with autonomous farm machinery and smart irrigation systems. Autonomous tractors operate without human intervention, freeing up farmers to focus on other tasks. Smart irrigation systems automatically adjust watering schedules based on real-time weather and soil moisture data, ensuring optimal watering for crops. The objective of this study is to demonstrate the capacity of digital farming to bring about significant changes in agricultural engineering techniques, in contrast to conventional approaches. It will examine the effects of digital farming on resource allocation, environmental sustainability, and the global food supply, thereby highlighting its potential for transformation. The research aims to inspire stakeholders in the agricultural sector to embrace digital farming as a transformative force, shaping the future of agricultural engineering practices for a more efficient, resilient, and prosperous agriculture sector.