Precision agriculture (PA) is a data-driven, technology-enabled farming management strategy that monitors, quantifies, and examines the requirements of specific crops and fields. A key aim of precision agricultural technologies is to optimize crop yield and quality, while also working to lower operating costs and minimize environmental impact. This approach not only enhances productivity but also promotes sustainable farming practices. In PA, it is essential to leverage effective monitoring through sensing technologies, implement robust management information systems, and proactively address both inter- and intravariability within cropping systems. Crop yield simulations using deep learning and machine learning (ML) techniques aid in understanding the combined effects of pests, nutrient and water shortages, and other field variables during the growing season. On the other hand, remote sensing techniques such as lidar imagery, radar, and multi- and hyperspectral data presents valuable opportunities to enhance yield predictions by improving the understanding of soil, climate, and other biophysical factors affecting crops. This paper aims to highlight key gaps and opportunities for future research, focusing on the evolving landscape of remote sensing and machine learning techniques employed to enhance predictions of crop yield. In future, PA is likely to include more focused use of sensor platforms and ML techniques can enhance the effectiveness of agricultural practices. Additionally, the development of hybrid systems that combine diverse ML approaches and signal processing techniques will pave the way for more innovative and efficient solutions in the field.
Saha et al. (2025) studied this question.