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United States Department of Agriculture, Agricultural Research Service, Genetics and Sustainable Agriculture Research Unit, Mississippi State, MI, USA
Interests: precision agriculture; application technology; remote sensing; unmanned aerial vehicle; data science and services; process modeling; optimization and control

Dr. Ali C. Gurbuz

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Department of Electrical and Computer Engineering, Mississippi State University, 406 Hardy Road, 216 Simrall Hall, Mississippi State, MS 39762, USA
Interests: machine learning; compressive sensing; computational imaging; radar and array signal processing; digital signal and image processing; remote sensing
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Dr. Xin Zhang

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Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Interests: smart agriculture; artificial intelligence in agriculture; agricultural robotics and machinery; remote sensing; computer vision; machine/deep learning; data science
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Dear Colleages,

Highly accurate and reliable crop yield estimation is critical for improved crop production process management and strategic planning. Remote sensing has been studied and developed for crop yield estimation. However, it is still being investigated with the aim of increasing the accuracy and reliability of crop yield estimation. This Special Issue aims to provide a perspective of the development and application of crop yield estimation through remote sensing from spaceborne, airborne and ground-based systems. Machine/deep learning has recently been brought in to increase the accuracy and reliability of crop yield estimation using remotely sensed data. This Special Issue invites authors to share their achievements on topics including but not limited to the following related to crop yield estimation through remote sensing: (1) at national or regional scale for crop production planning; (2) at farm or field scale for precision agriculture operations; (3) assimilation remote sensing data into crop models; (4) developing specialized machine/deep learning schemes and algorithms.

Dr. Yanbo Huang
Dr. Ali C. Gurbuz
Dr. Xin Zhang
Guest Editors

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Keywords

crop yield estimation
remote sensing
machine learning

Published Papers (2 papers)

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Research

Open AccessArticle

In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images

and

Agronomy 2022, 12(12), 3176; https://doi.org/10.3390/agronomy12123176 - 15 Dec 2022

Viewed by 749

Abstract

Crop growth and yield monitoring are essential for food security and agricultural economic return prediction. Remote sensing is an efficient technique for measuring growing season crop canopies and providing information on the spatial variability of crop yields. In this study, ten vegetation indices (VIs) derived from time series PlanetScope and Sentinel-2 images were used to investigate the potential to estimate corn grain yield with different regression methods. A field-scale spatial crop yield prediction model was developed and used to produce yield maps depicting spatial variability in the field. Results from this study clearly showed that high-resolution PlanetScope satellite data could be used to detect the corn yield variability at field level, which could explain 15% more variability than Sentinel-2A data at the same spatial resolution of 10 m. Comparison of the model performance and variable importance measure between models illustrated satisfactory results for assessing corn productivity with VIs. The green chlorophyll vegetation index (GCVI) values consistently produced the highest correlations with corn yield, accounting for 72% of the observed spatial variation in corn yield. More reliable quantitative yield estimation could be made using a multi-linear stepwise regression (MSR) method with multiple VIs. Good agreement between observed and predicted yield was achieved with the coefficient of determination value being 0.81 at 86 days after seeding. The results would help farmers and decision-makers generate predicted yield maps, identify crop yield variability, and make further crop management practices timely. Full article

(This article belongs to the Special Issue Crop Yield Estimation through Remote Sensing Data)

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Open AccessArticle

Predicting In-Season Corn Grain Yield Using Optical Sensors

and

Agronomy 2022, 12(10), 2402; https://doi.org/10.3390/agronomy12102402 - 04 Oct 2022

Cited by 1 | Viewed by 1802

Abstract

In-season sensing can account for field variability and improve nitrogen (N) management; however, opportunities exist for refinement. The purpose of this study was to compare different sensors and vegetation indices (VIs) (normalized difference vegetation index (NDVI); normalized difference red edge (NDRE); Simplified Canopy Chlorophyll Content Index (SCCCI)) at various corn stages to predict in-season yield potential. Additionally, different methods of yield prediction were evaluated where the final yield was regressed against raw or % reflectance VIs, relative VIs, and in-season yield estimates (INSEY, VI divided by growing degree days). Field experiments at eight-site years were established in Mississippi. Crop reflectance data were collected using an at-leaf SPAD sensor, two proximal sensors: GreenSeeker and Crop Circle, and a small unmanned aerial system (sUAS) equipped with a MicaSense sensor. Overall, relative VI measurements were superior for grain yield prediction. MicaSense best predicted yield at the VT-R1 stages (R² = 0.78–0.83), Crop Circle and SPAD at VT (R² = 0.57 and 0.49), and GreenSeeker at V10 (R² = 0.52). When VIs were compared, SCCCI (R² = 0.40–0.49) outperformed other VIs in terms of yield prediction. Overall, the best grain yield prediction was achieved using the MicaSense-derived SCCCI at the VT-R1 growth stages. Full article

(This article belongs to the Special Issue Crop Yield Estimation through Remote Sensing Data)

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