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Agronomy
Special Issues
Crop Yield Estimation through Remote Sensing Data
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Crop Yield Estimation through Remote Sensing Data

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 18184

Special Issue Editors

Dr. Yanbo Huang

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Ali C. Gurbuz

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Zhang

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: smart/digital agriculture; artificial intelligence in agriculture; crop prediction models; UAV/UGV swarm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crop yield estimation
  • remote sensing
  • machine learning

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Related Special Issue

Published Papers (4 papers)

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Research

22 pages, 3254 KiB  
Article
Multi-Stage Corn Yield Prediction Using High-Resolution UAV Multispectral Data and Machine Learning Models
by Chandan Kumar, Partson Mubvumba, Yanbo Huang, Jagman Dhillon and Krishna Reddy
Agronomy 2023, 13(5), 1277; https://doi.org/10.3390/agronomy13051277 - 28 Apr 2023
Cited by 40 | Viewed by 5621
Abstract
Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and [...] Read more.
Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R2 = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R2 = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R2 = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R2 and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data. Full article
(This article belongs to the Special Issue Crop Yield Estimation through Remote Sensing Data)
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18 pages, 3595 KiB  
Article
Estimation of Strawberry Crop Productivity by Machine Learning Algorithms Using Data from Multispectral Images
by Larissa Silva de Oliveira, Renata Castoldi, George Deroco Martins and Matheus Henrique Medeiros
Agronomy 2023, 13(5), 1229; https://doi.org/10.3390/agronomy13051229 - 27 Apr 2023
Cited by 8 | Viewed by 2748
Abstract
Currently, estimations of strawberry productivity are conducted manually, which is a laborious and subjective process. The use of more efficient and precise estimation methods would result in better crop management. The objective of this study was to assess the performance of two regression [...] Read more.
Currently, estimations of strawberry productivity are conducted manually, which is a laborious and subjective process. The use of more efficient and precise estimation methods would result in better crop management. The objective of this study was to assess the performance of two regression algorithms-Linear Regression and Support Vector Machine—in estimating the average weight and number of fruits and the number of leaves on strawberry plants, using multispectral images obtained by a remotely piloted aircraft (RPA). The experiment, which was conducted in the experimental area of the Botany Laboratory at the Federal University of Uberlândia-Monte Carmelo Campus (Universidade Federal de Uberlândia, Campus Monte Carmelo), was carried out using a randomized block design with six treatments and four replications. The treatments comprised six commercial strawberry varieties: San Andreas, Albion, PR, Festival, Oso Grande, and Guarani. Images were acquired on a weekly basis and then preprocessed to extract radiometric values for each plant in the experimental area. These values were then used to train the production prediction algorithms. During the same period, data on the average fruit weight, number of fruits per plant, and number of leaves were collected. The total fruit weight in the field was 48.08 kg, while the linear regression (LR) and Support Vector Machine (SVM) estimates were 48.04 and 43.09 kg, respectively. The number of fruits obtained in the field was 4585, and the number estimated by LR and SVM algorithms was 4564 and 3863, respectively. The number of leaves obtained in the field was 10,366, and LR and SVM estimated 10,360 and 10,171, respectively. It was concluded that LR and SVM can estimate strawberry production and the number of fruits and leaves using multispectral unmanned aerial vehicle (UAV) images. The LR algorithm was the most efficient in estimating production, with 99.91% accuracy for average fruit weight, 99.55% for the number of fruits and 99.94% for the number of leaves. SVM exhibited 89.62% accuracy for average fruit weight, 84.26% for the number of fruits, and 98.12% for the number of leaves. Full article
(This article belongs to the Special Issue Crop Yield Estimation through Remote Sensing Data)
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18 pages, 28562 KiB  
Article
In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images
by Fenling Li, Yuxin Miao, Xiaokai Chen, Zhitong Sun, Kirk Stueve and Fei Yuan
Agronomy 2022, 12(12), 3176; https://doi.org/10.3390/agronomy12123176 - 15 Dec 2022
Cited by 18 | Viewed by 3454
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 [...] Read more.
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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10 pages, 874 KiB  
Article
Predicting In-Season Corn Grain Yield Using Optical Sensors
by Camden Oglesby, Amelia A. A. Fox, Gurbir Singh and Jagmandeep Dhillon
Agronomy 2022, 12(10), 2402; https://doi.org/10.3390/agronomy12102402 - 4 Oct 2022
Cited by 10 | Viewed by 4194
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 [...] Read more.
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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