A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics
Canola (Brassica napus L.) yield prediction using a combined application of small unmanned aerial system (sUAS) and vegetation indices (VIs) have gained significant attention. In recent years, major studies have demonstrated the potential of developing new VIs for predicting canola yield. However, s...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003022 |
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| author | Nitin Rai Harsh Pathak Maria Villamil Mahecha Dennis R. Buckmaster Yanbo Huang Paul Overby Xin Sun |
| author_facet | Nitin Rai Harsh Pathak Maria Villamil Mahecha Dennis R. Buckmaster Yanbo Huang Paul Overby Xin Sun |
| author_sort | Nitin Rai |
| collection | DOAJ |
| description | Canola (Brassica napus L.) yield prediction using a combined application of small unmanned aerial system (sUAS) and vegetation indices (VIs) have gained significant attention. In recent years, major studies have demonstrated the potential of developing new VIs for predicting canola yield. However, such indices may perform optimally on the specific farms for which they have been designed and may fail to generalize to a new agronomic scenario. Therefore, this study aims to conduct a comprehensive analysis on the application of existing VIs that could be used to predict potential canola yield on a commercial farm, thereby eliminating the need to develop new indices from scratch. In this research study, over twenty-seven VIs were extracted from two sUAS imagery captured during peak flowering and seed development stages of canola. The extracted features were fed to four conventional machine learning (ML) classifiers with appropriate hyperparameter tuning approaches. Additionally, to perform a comparative test with neural network-based deep learning (DL) architectures, a convolutional neural network-1-dimensional (CNN-1D_Canola) model was developed to train data points and predict canola yield. These models were trained on the yield maps interpolated using three approaches, based on the ground truth yield data points obtained from a harvester. Results suggest that peak flowering is the best stage to predict canola yield. Additionally, a combination of kriging-based yield maps with three best features, canola ratio index (CRI), canola index (CI), and structure intensive vegetation index (SIPI) indices, trained using support vector machine (R2=0.68), multi-layer perceptron (R2=0.7), and CNN_1D_Canola (R2=0.66) have the potential to predict canola yield based on spectral image-based features. This study highlights the potential of predicting canola yield for a commercial farm using a combined application of sUAS imagery and VIs. The promising performance of all the models coupled with a comprehensive hyperparameter tuning approaches suggests its applicability in predicting canola yield in real field conditions. |
| format | Article |
| id | doaj-art-65d1114af2eb4d3dbd7701a2a7fd985e |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-65d1114af2eb4d3dbd7701a2a7fd985e2025-08-20T02:38:39ZengElsevierSmart Agricultural Technology2772-37552024-12-01910069810.1016/j.atech.2024.100698A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analyticsNitin Rai0Harsh Pathak1Maria Villamil Mahecha2Dennis R. Buckmaster3Yanbo Huang4Paul Overby5Xin Sun6Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, United StatesDepartment of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, United StatesDepartment of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, United StatesDepartment of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, United StatesUSDA ARS, Crop Production Systems Research Unit, 141 Experiment Station Road, PO Box 350, Stoneville, MS 38776, United StatesLee Farms, Wolford, ND, 58385, United StatesDepartment of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, United States; Corresponding author.Canola (Brassica napus L.) yield prediction using a combined application of small unmanned aerial system (sUAS) and vegetation indices (VIs) have gained significant attention. In recent years, major studies have demonstrated the potential of developing new VIs for predicting canola yield. However, such indices may perform optimally on the specific farms for which they have been designed and may fail to generalize to a new agronomic scenario. Therefore, this study aims to conduct a comprehensive analysis on the application of existing VIs that could be used to predict potential canola yield on a commercial farm, thereby eliminating the need to develop new indices from scratch. In this research study, over twenty-seven VIs were extracted from two sUAS imagery captured during peak flowering and seed development stages of canola. The extracted features were fed to four conventional machine learning (ML) classifiers with appropriate hyperparameter tuning approaches. Additionally, to perform a comparative test with neural network-based deep learning (DL) architectures, a convolutional neural network-1-dimensional (CNN-1D_Canola) model was developed to train data points and predict canola yield. These models were trained on the yield maps interpolated using three approaches, based on the ground truth yield data points obtained from a harvester. Results suggest that peak flowering is the best stage to predict canola yield. Additionally, a combination of kriging-based yield maps with three best features, canola ratio index (CRI), canola index (CI), and structure intensive vegetation index (SIPI) indices, trained using support vector machine (R2=0.68), multi-layer perceptron (R2=0.7), and CNN_1D_Canola (R2=0.66) have the potential to predict canola yield based on spectral image-based features. This study highlights the potential of predicting canola yield for a commercial farm using a combined application of sUAS imagery and VIs. The promising performance of all the models coupled with a comprehensive hyperparameter tuning approaches suggests its applicability in predicting canola yield in real field conditions.http://www.sciencedirect.com/science/article/pii/S2772375524003022CanolaData analyticsHyperparameter tuningMachine learningNeural-networkRemote sensing |
| spellingShingle | Nitin Rai Harsh Pathak Maria Villamil Mahecha Dennis R. Buckmaster Yanbo Huang Paul Overby Xin Sun A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics Smart Agricultural Technology Canola Data analytics Hyperparameter tuning Machine learning Neural-network Remote sensing |
| title | A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics |
| title_full | A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics |
| title_fullStr | A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics |
| title_full_unstemmed | A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics |
| title_short | A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics |
| title_sort | case study on canola brassica napus l potential yield prediction using remote sensing imagery and advanced data analytics |
| topic | Canola Data analytics Hyperparameter tuning Machine learning Neural-network Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003022 |
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