Innovative Approaches in Remote Sensing for Precise Crop Yield Estimation: Advancements, Applications, and Future Directions

Agriculture faces the urgent challenge of accurately predicting crop yields, particularly under the pressures of climate change and the growing demands on food production systems to ensure global food security. Traditionally, techniques for estimating crop yields have relied on field surveys, statistical methods, and expert assessments, which can be time-consuming, costly, and geographically limited. In contrast, remote sensing technologies, such as satellite-based multispectral imaging and UAV-based sensors, offer a significant breakthrough. These tools provide comprehensive, high-resolution, real-time analyses of crop health, soil moisture, and other critical farming parameters. The integration of these technological innovations with advanced machine learning and deep learning algorithms has started a new era in agriculture, allowing for precise and economical agricultural forecasting, thus empowering farmers and policymakers.

This Research Topic aims to tackle the pressing challenges associated with traditional crop yield estimation methods and to demonstrate the potential of remote sensing technologies as essential tools for achieving more accurate and scalable predictions. By leveraging the capabilities of satellite imagery, UAV-based sensors, and advanced data analytics, this Topic seeks to enhance the precision and timeliness of crop yield forecasts, which are pivotal for improving agricultural productivity and sustainability on a global scale.

The scope of this Research Topic includes exploring the breadth of applications and innovations brought forth by remote sensing technologies. We invite comprehensive contributions on the following specific themes:

• Satellite and UAV-based remote sensing techniques for monitoring crop yield

• Advanced machine learning and AI models dedicated to predicting crop yield

• Innovative data fusion approaches that integrate remote sensing data with climatic inputs

• Detailed case studies and real-world applications demonstrating the efficacy of yield estimation models

• Strategies to address issues related to data quality, resolution, and interference by cloud coverage

• Evaluating the impact of changing climate conditions on the accuracy of remote sensing-based crop yield predictions

Further contributions include original research articles, detailed reviews, and case studies that present novel insights into the application of remote sensing techniques, combined with machine learning or other advanced computational models, to significantly enhance the accuracy of crop yield estimations. By embracing a multidisciplinary approach, this Research Topic aims to foster innovation and guide the future of agricultural practices towards sustainability and resilience.

Keywords: Remote Sensing, Crop Monitoring, UAVs, Drones, Image Processing, Crop Yield Estimation, AI for Agriculture

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.