Special Issue on Spatiotemporal Intelligence for Crop Yield and Quality Prediction

Crop yield and quality are key determinants of agricultural productivity, food security, and sustainable crop production. However, accurately predicting crop yield and quality remains challenging due to complex interactions among crop genetics, environmental conditions, and management practices.

Recent advances in remote sensing, artificial intelligence, crop modeling, and multi-source data integration have created new opportunities for developing intelligent approaches to monitor, predict, and improve crop yield and quality. These technologies enable researchers to move beyond traditional statistical approaches toward dynamic, spatially explicit, and mechanistically informed prediction systems.

This Special Issue of The Crop Journal aims to bring together innovative research on information-driven approaches for crop yield and quality assessment, prediction, and decision support. We welcome studies integrating remote sensing, field observations, environmental information, crop models, artificial intelligence, and data assimilation techniques to improve understanding and prediction of crop production outcomes.

Research addressing the impacts of climate extremes and environmental stresses on crop yield and quality is particularly encouraged. Studies focusing on stress detection, yield and quality loss attribution, uncertainty analysis, and the translation of predictive models into practical agronomic decisions are also welcomed.

Topics covered:

These will include, but are not limited to:

• Crop yield prediction and spatial-temporal monitoring;
• Crop quality assessment and prediction using intelligent approaches;
• Remote sensing and multi-source data fusion for crop production evaluation;
• Machine learning, deep learning, and artificial intelligence applications in crop science;
• Integration of remote sensing with process-based crop growth models;
• Data assimilation and hybrid modeling approaches;
• Impacts or attribution of climate extremes and environmental stresses on yield and quality;
• Decision-support systems for precision agriculture and crop management;
• Applications of predictive models in production planning and post-harvest evaluation.

Important Deadlines:

Submission deadline (for full papers): 31 May 2027

Peer review due: November 2026 to July 2027

Publication date: September 2027

Individual papers will be published on ScienceDirect immediately following acceptance.

Guest Editors:

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online; please select the special issue “Spatiotemporal Intelligence for Crop Yield and Quality Prediction”.

Manuscripts will be reviewed and published on a first-come, first-served basis. Once all accepted manuscripts are published, they will be gathered together in this special issue.