Integrated planning for planting and harvesting sugarcane and energy-cane for the production of sucrose and energy

Poltroniere, Sônia Cristina; Filho, Angelo Aliano; Caversan, Amanda Suellen; Balbo, Antônio Roberto; Florentino, Helenice de Oliveira

doi:10.1016/j.compag.2020.105956

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…In addition, stalks became more fibrous, supporting “isoporization” ( Morais et al., 2015 ). Such a phenomenon indicates a reduction in water and sugar, making harvesting inefficient and costly ( Poltroniere et al., 2021 ). By monitoring the field and mapping °Brix and Purity, we can offer stakeholders possibilities to optimize their on-farm management and agribusiness models.…”

Section: Discussionmentioning

confidence: 99%

UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane

et al. 2023

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“…Three recent papers consider the integrated decision on planting and harvesting (Carvajal et al., 2019; Florentino et al., 2020; Poltroniere et al., 2021). Poltroniere et al.…”

Section: Analysis and Discussion Of The Selected Papersmentioning

confidence: 99%

“…Poltroniere et al. (2021) present a new mixed integer model taking into account decisions on planting and harvesting of sugarcane, as well as energy‐cane that helps in the production of energy and second‐generation ethanol. A decomposition heuristic is proposed to solve large instances.…”

Section: Analysis and Discussion Of The Selected Papersmentioning

confidence: 99%

“…Three recent papers consider the integrated decision on planting and harvesting (Carvajal et al, 2019;Florentino et al, 2020;Poltroniere et al, 2021). Poltroniere et al (2021) present a new mixed integer model taking into account decisions on planting and harvesting of sugarcane, as well as energy-cane that helps in the production of energy and second-generation ethanol. A decomposition heuristic is proposed to solve large instances.…”

Section: Integrating Planting and Harvestingmentioning

confidence: 99%

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A review of mathematical optimization models applied to the sugarcane supply chain

Teixeira¹,

Rangel²,

Florentino³

et al. 2021

Int Trans Operational Res

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Sugarcane is a product of great economic relevance and, as well as its use for the production of different types of sugar, it is also a renewable source for the production of bio‐fuels, other bio‐products, and electricity. In this context, efficient planning and technology changes are necessary to ensure high productivity, competitiveness and harmony with economic, social, and environmental issues. Consequently, this sector needs technical and scientific support. Mathematical optimization models can be useful to build efficient planning tools for the activities involved in the sugarcane supply chain, helping to address these challenges. This paper describes the sugarcane supply chain and presents a literature review of mathematical optimization models that have been proposed to represent its main stages: planting, harvesting, transporting, industrial processing, and marketing. The findings show that several models have been proposed to represent these five stages, however, most of them have not been thoroughly explored from the point of view of strength of the models and solution methods. Moreover, the harvesting stage is the one that has received more attention than the other stages. In the integration of two or more stages, there are many challenges yet to be addressed by mathematical optimization models. This review contributes to a better understanding of the sugar‐energy sector and opens up new perspectives for future investigations.

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“…Estos artículos tienen una particularidad y es que son los únicos que involucran desempeño de los cultivos de caña de azúcar como variable que depende de las socas y la edad del cultivo; también, se incluye el ciclo de vida del cultivo desde la siembra y sus múltiples cortes. Adicionalmente, en la revisión sistemática realizada por Teixeira et al (2021) se identifican solo tres artículos en los últimos 10 años en los cuales se involucran las actividades de siembra y cosecha (Carvajal et al, 2019;Florentino et al, 2020;Poltroniere et al, 2021).…”

Section: Integración De Operaciones En La Sbscunclassified

Biofuels technologies: An overview of feedstocks, processes, and technologies

Ziolkowska

2020

Biofuels for a More Sustainable Future

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Integrated planning for planting and harvesting sugarcane and energy-cane for the production of sucrose and energy

Cited by 16 publications

References 20 publications

UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane

UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane

A review of mathematical optimization models applied to the sugarcane supply chain

Biofuels technologies: An overview of feedstocks, processes, and technologies

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