Deep Learning Methods for Crop Monitoring and Crop Yield Prediction

Remote sensing and statistical methods have been extensively used for crop assessment at regional, national, and global scales, with many studies informing national and regional agricultural policies. The sensing platforms have been largely satellite-based. However, with greater resolutions of sensing equipment and proliferation of UAVs, field scale analyses have become an additional possibility.

Recent advances in data acquisition capabilities, computational platforms (especially utilizing massive parallel processing using high-performance GPU boards), and Big Data structures have enabled the emergence of deep learning architectures, capable of performing tasks previously not feasible using conventional machine learning techniques. Characteristic features of these architectures include relying on extensive heterogenous data sets and learning from raw data rather than requiring separate feature extraction stage.

This Special Issue focuses on the latest research advances in assessing crop development and predicting crop yield throughout the growing season in open fields and greenhouses. With exploding applications of deep learning in agriculture, this Special Issue would like to invite submissions that specifically focus on target detection, qualitative and quantitative metrics for crop condition assessment, and crops yield estimation and prediction from remote sensing using deep learning techniques. Submissions in the form of research articles, reviews, perspectives, and case studies are all welcome. We especially invite studies that promote usability in terms of access to code or developed tools.

Prof. Tarmo Lipping
Dr. Miao Zhang
Dr. Nathaniel Narra
Guest Editors

Manuscript Submission Information

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