UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane
Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer sig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1114852/full |
| _version_ | 1828048635761262592 |
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| author | Marcelo Rodrigues Barbosa Júnior Marcelo Rodrigues Barbosa Júnior Bruno Rafael de Almeida Moreira Romário Porto de Oliveira Luciano Shozo Shiratsuchi Rouverson Pereira da Silva |
| author_facet | Marcelo Rodrigues Barbosa Júnior Marcelo Rodrigues Barbosa Júnior Bruno Rafael de Almeida Moreira Romário Porto de Oliveira Luciano Shozo Shiratsuchi Rouverson Pereira da Silva |
| author_sort | Marcelo Rodrigues Barbosa Júnior |
| collection | DOAJ |
| description | Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products. |
| first_indexed | 2024-04-10T18:59:16Z |
| format | Article |
| id | doaj.art-89cfad5542154b11a990b0b12dd1cc6e |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-10T18:59:16Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-89cfad5542154b11a990b0b12dd1cc6e2023-01-31T11:58:29ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-01-011410.3389/fpls.2023.11148521114852UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcaneMarcelo Rodrigues Barbosa Júnior0Marcelo Rodrigues Barbosa Júnior1Bruno Rafael de Almeida Moreira2Romário Porto de Oliveira3Luciano Shozo Shiratsuchi4Rouverson Pereira da Silva5Department of Engineering and Mathematical Sciences, School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), São Paulo, BrazilAgCenter, School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, LA, United StatesDepartment of Engineering and Mathematical Sciences, School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), São Paulo, BrazilDepartment of Engineering and Mathematical Sciences, School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), São Paulo, BrazilAgCenter, School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, LA, United StatesDepartment of Engineering and Mathematical Sciences, School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), São Paulo, BrazilPredicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.https://www.frontiersin.org/articles/10.3389/fpls.2023.1114852/fullremote sensingbrixsucroseripeningSaccharum spp.smart harvest |
| spellingShingle | Marcelo Rodrigues Barbosa Júnior Marcelo Rodrigues Barbosa Júnior Bruno Rafael de Almeida Moreira Romário Porto de Oliveira Luciano Shozo Shiratsuchi Rouverson Pereira da Silva UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane Frontiers in Plant Science remote sensing brix sucrose ripening Saccharum spp. smart harvest |
| title | UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane |
| title_full | UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane |
| title_fullStr | UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane |
| title_full_unstemmed | UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane |
| title_short | UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane |
| title_sort | uav imagery data and machine learning a driving merger for predictive analysis of qualitative yield in sugarcane |
| topic | remote sensing brix sucrose ripening Saccharum spp. smart harvest |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1114852/full |
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