DIEGO DOMINGOS DELLA JUSTINA
SUGARCANE SUGAR CONTENT ESTIMATION USING
REMOTE SENSING DATA
MODELO ESPECTRAL DE ESTIMATIVA DE RENDIMENTO
DA CANA-DE-AÇÚCAR
CAMPINAS 2018
SUGARCANE SUGAR CONTENT ESTIMATION USING
REMOTE SENSING DATA
MODELO ESPECTRAL DE ESTIMATIVA DE RENDIMENTO
DA CANA-DE-AÇÚCAR
Thesis presented to the School of Agricultural Engineering of the University of Campinas in partial fulfillment of the requirements for degree of Doctor in Agricultural Engineering, in Systems Management in Agriculture and Rural Development.
Tese apresentada à Faculdade de Engenharia Agrícola da Universidade Estadual de Campinas como parte dos requisitos exigidos para a obtenção do título de Doutor em Engenharia Agrícola, na Área de Gestão de Sistemas na Agricultura e Desenvolvimento Rural.
Supervisor/Orientador: Prof. Dr. Jansle Vieira Rocha
Cosupervisor/Coorientador: Prof. Dr. Rubens Augusto Camargo Lamparelli
ESTE EXEMPLAR CORRESPONDE À VERSÃO FINAL DA TESE DEFENDIDA PELO ALUNO DIEGO DOMINGOS DELLA JUSTINA, E ORIENTADA PELO PROF. DR. JANSLE VIEIRA ROCHA.
CAMPINAS 2018
Ficha catalográfica
Universidade Estadual de Campinas Biblioteca da Área de Engenharia e Arquitetura
Luciana Pietrosanto Milla - CRB 8/8129
Justina, Diego Domingos Della, 1987-
J984sJus Sugarcane sugar content estimation using remote sensing data / Diego
Domingos Della Justina. – Campinas, SP : [s.n.], 2018. JusOrientador: Jansle Vieira Rocha.
JusCoorientador: Rubens Augusto Camargo Lamparelli.
JusTese (doutorado) – Universidade Estadual de Campinas, Faculdade de
Engenharia Agrícola.
Jus1. Agricultura de precisão. 2. Sensoriamento remoto. 3. Cana-de-açúcar. 4.Aprendizado de máquina. 5. Mineração de dados. I. Rocha, Jansle Vieira, 1961-. II. Lamparelli, Rubens Augusto Camargo, 1955-. III. Universidade Estadual de Campinas. Faculdade de Engenharia Agrícola. IV. Título.
Informações para Biblioteca Digital
Título em outro idioma: Estimativa do teor de açúcar da cana-de-açúcar usando dados de sensoriamento remoto Palavras-chave em inglês: Precision Agriculture Remote sensing Sugarcane Machine learning Data mining
Área de concentração: Gestão de Sistemas na Agricultura e Desenvolvimento Rural Titulação: Doutor em Engenharia Agrícola
Banca examinadora:
Jansle Vieira Rocha [Orientador] Glauco De Souza Rolim
Jerry Adriani Johann
Luiz Henrique Antunes Rodrigues Paulo Sérgio Graziano Magalhães Data de defesa: 04-07-2018
Faculdade de Engenharia Agrícola da Universidade Estadual de Campinas.
________________________________________________________________ Prof. Dr. Jansle Vieira Rocha – Presidente e Orientador
FEAGRI/UNICAMP
________________________________________________________________ Prof. Dr. Glauco de Souza Rolim – Membro Titular
UNESP/Jaboticabal
________________________________________________________________ Prof. Dr. Jerry Adriani Johann – Membro Titular
UNIOESTE/Cascavel
_______________________________________________________________ Prof. Dr. Luiz Henrique Antunes Rodrigues – Membro Titular
UNICAMP/Campinas
________________________________________________________________ Prof. Dr. Paulo Sérgio Graziano Magalhães – Membro Titular
UNICAMP/Campinas
A Ata da defesa com as respectivas assinaturas dos membros encontra-se no processo de vida acadêmica da discente.
I would like to acknowledge the following people and organizations for their assistance during these four years dedicated to development of this thesis.
I would like to thank my parents, I’m very grateful for their support and for investing in me. To Priscila Grutzmacher, my beloved companion, for her support and friendship during the course of this project. I also would like to thank her family, especially Dr. Robson Barizon and Vanessa Hachiman for their hospitality and friendship.
I would like to thank my advisors, professors Jansle Rocha, Rubens Lamparelli and Paulo Magalhães, for providing the unique opportunity of learning from a collaborative project between Unicamp and Hitachi Ltd. I would like to thank their support, also for trusting me with important decisions regarding the project.
I thank the National Council for Scientific and Technological Development (CNPq), and Hitachi Ltd. for the financial support. I would also like to thank Hitachi personal, Takaaki Mizukami, Yu Zhao, Raphael Castilho Gil, Keiko Ioshitake as well as other researchers that were part of the project in the past, for knowledge exchanged.
I thank the São João mill’s personnel, Humberto César Carrara Neto, Fernando Palma, Adriano Donizeti Pinheiro, Renata Soares and Julio Cesar Mussarelli, for their support and data sharing. I would like to acknowledge my colleagues Carlos Wachholz de Souza, Danilo Galdino de Figueiredo, Marcelo Silva, Márcio Roberto da Silva Melo, Priscila Grutzmacher, Ramses Amadeus Molijn, Thiago Luiz Brasco, Victor Manabe, Walter Rossi Cervi for the assistance with the field surveying, also Felipe Ferreira Bocca and Peter Groenendijk for the methodology review.
“Advances in medicine and agriculture have
saved vastly more lives than have been
lost in the wars in history.”
~ Carl Edward Sagan
“Fundamentally learning about the world
through data is really, really cool.”
~ Hadley Wickham, prolific R developer
Sugarcane yield is determined not only by fresh weight but also by the sugar content. Foreknowledge about sugar content (Brix) is imperative to determine the plant maturity and, consequently, the productivity of its derivates, i.e., sugar and ethanol. Data driven decision-make systems have been widely adopted by the sugarcane industry. Mills still relies on methods such as field surveys determine the quality parameters such as Brix. Due to the extension of the cultivated area and the poor intra-field accessibility because of dense plant population, field survey is considered very inefficient method. Many monitoring initiatives focus on the use of growth models for yield estimations, but they are difficult to apply in regional estimates due to data availability and limitations related to the model itself. Alternatively, research using empirical models built using machine learning techniques have shown good performance on sugarcane yield estimations, but the dependence of multiple data sources is a major drawback. Because of its capabilities of retrieving vegetation biophysical parameters at regional scales, remote sensing is considered a valuable tool for crop field surveillance. For this reason, we investigated the feasibility of retrieving Brix information from sugarcane spectral response obtained from spaceborne platforms. To this end we evaluated the predictive performance of regression models built using multispectral reflectance data, derived from satellite sensors and Brix values obtained from ground-based observations. We also evaluated the correspondence between spectral data and sugarcane physiological processes related to sugar accumulation, to ensure the remote sensing capabilities of retrieving biophysical parameters. Given the large number of available remote sensing platforms, we tested two satellite sensors, the RapidEye Multi Spectral Imager (MSI) and the Landsat-8 Operational Land Imager (OLI), each one with different set characteristics regarding spectral, spatial and temporal resolutions. The performance of MSI and OLI sensors data resulted in a mean absolute error (MAE) of 0.315 and 0.376 respectively, indicating no significant difference between sensors. Comparing to the performance of a traditional approach, which is simply predicting the Brix from average Brix value observed (0.621), MAE from both sensors was significantly smaller. Since the empirical models based on spectral data were more accurate, the use of remote sensing data is feasible to estimate sugarcane quality. Multiple transfer functions were modeled empirically using multiple machine learning techniques that use spectral data to predict Brix variation in the São João Mill area for the 2013 - 2016 crop seasons. The performance of the models was evaluated across techniques using both residual-based and ranking-based metrics. According to the
Brix using the average of the overserved data. According to the ranking-based metric, the model’s performance ranged from a GINI coefficient of 0.512 to 0.707, showing clear improvement compared to a null model GINI of 0.204. Our results indicate that spectral data can be used to estimate sugarcane Brix content.
Keywords: Reflectance Spectroradiometry; Empirical Modeling; Sugarcane Brix, GLMM - Generalized linear mixed models; Data Mining.
O rendimento da cana-de-açúcar é determinado não apenas pelo peso fresco, mas também pelo teor de açúcar. O conhecimento prévio sobre o teor açúcar (Brix) é imperativo para determinar a maturidade da planta e, consequentemente, a produtividade de seus derivados, ou seja, açúcar e etanol. Os sistemas de decisão baseados em dados têm sido amplamente adotados pela indústria da cana-de-açúcar. No entanto, as usinas ainda dependem de métodos como levantamentos de campo para determinar os parâmetros de produtividade, como o Brix. Devido à extensão da área cultivada e à baixa acessibilidade das áreas cultivadas, devido à densidade populacional de plantas, o levantamento de campo é considerado um método muito ineficiente. Muitas iniciativas de monitoramento concentram-se no uso de modelos de crescimento para estimativas de rendimento, mas são difíceis de aplicar em estimativas regionais devido à disponibilidade de dados e limitações relacionadas ao próprio modelo. Pesquisas utilizando modelos empíricos construídos usando técnicas de aprendizado de máquina mostraram bom desempenho em estimativas de produtividade de cana-de-açúcar, podendo ser uma alternativa viável aos modelos de crescimento, mas a dependência de múltiplas fontes de dados dos modelos empíricos é uma grande desvantagem. Devido à sua capacidade de estimar parâmetros biofísicos da vegetação em escalas regionais, o sensoriamento remoto é considerado uma ferramenta valiosa para o monitoramento das áreas cultivadas. Por esse motivo, investigamos a viabilidade da estimativa do Brix através da resposta espectral da cana obtida de sensores orbitais. Para este fim, avaliamos o desempenho preditivo de modelos de regressão construídos usando dados de reflectância multiespectral, derivados de sensores de satélite e valores Brix obtidos a partir de observações terrestres. Também avaliamos a correspondência entre os dados espectrais e os processos fisiológicos da cana-de-açúcar relacionados ao acúmulo de açúcar, para avaliar a capacidade do sensoriamento remoto de estimar parâmetros biofísicos. Dado o grande número de plataformas de sensoriamento remoto, foram testados dois sensores de satélite, o RapidEye Multi Spectral Imager (MSI) e o LANDAT-8 Operational Land Imager (OLI), cada um com diferentes características de resolução espectral, espacial e temporal. O desempenho dos dados dos sensores MSI e OLI resultou em um erro absoluto médio (MAE) de 0,315 e 0,376, respectivamente, indicando nenhuma diferença significativa entre os sensores. Comparando com o desempenho de uma abordagem tradicional, que é simplesmente predizer o Brix do valor de Brix médio observado (0,621), o MAE de ambos os sensores foi significativamente menor. Como os modelos empíricos baseados em dados espectrais foram
várias técnicas de aprendizado de máquina que usam dados espectrais para prever a variação Brix na área da Usina São João para as safras 2013 - 2016. O desempenho dos modelos foi avaliado entre as técnicas usando métricas com base em métricas de resíduo e de ranqueamento. De acordo com a avaliação dos resíduos, o MAE dos variaram de 1,274 a 1,282. Seus desempenhos ficaram próximos a um modelo nulo MAE de 1,414, o que corresponde a prever o Brix utilizando a média dos dados observados. De acordo com a métrica de ranqueamento, o desempenho do modelo variou de um coeficiente de GINI de 0,512 a 0,707, mostrando melhora clara em comparação com um modelo nulo GINI de 0,204. Nossos resultados indicam que dados espectrais podem ser usados para estimar o conteúdo de Brix de cana-de-açúcar.
Palavras-chave: Espectrorradiometria de Refletância; Modelagem Empírica; Brix Cana-de-açúcar; GLMM – Modelos Lineares Generalizados Mistos; Mineração de Dados.
Figure 1. Sugarcane development phases for plant-cane and ratoon cycles (CHEAVEGATTI-GIANOTTO et al., 2011). ... 21 Figure 2. Process diagram of sucrose cycle in sugarcane (WANG et al., 2013b) ... 23 Figure 3. Leaf spectral reflectance main dominant factors (adapted from (PETERSON et al., 1988; ADAM; MUTANGA; RUGEGE, 2010). The spectral curve corresponds to the reflectance of a mature (green) and senescent (dry) sugarcane leaf, measured by a contact probe for single leaf. ... 27 Figure 4. Location of study sites and sampling grid arrangement. ... 49 Figure 5. Spectral band widths and positions of Landsat-8 (OLI) and RapidEye (MSI) sensors. Spectral reflectance of mature (green) and senescent (dry) sugarcane leaf’s, measured by a contact probe for single leaf. *OLI Band 1 (Ultra Blue) is omitted. ... 50 Figure 6. Datasets temporal correspondences, according to the day of season. ... 51 Figure 7. Plot of predictor’s slopes for RapidEye MSI (left) and Landsat-8 OLI (right). The average slope of each predictor is represented by a black circle, and a horizontal line, which corresponds to its symmetric 95% confidence interval. ... 54 Figure 8. Marginal effect displays for the interaction of site (A, B) and DOS (303, 331, 366) in the GLMM regression model fit to the Brix. The axis is labelled on observed an estimated Brix, a 95% confidence interval is drawn around the estimated effect, and the respective slope value (β) is displayed for each group. ... 58 Figure 9. Observed vs. predicted Brix scatter plots of data from the regression output obtained from RapidEye (left) and Landsat-8 (right). R² - Coefficient of determination. MAE – Mean Absolute Error. MAD – Mean Absolute Deviation. ... 59 Figure 10. Study site map, with geographic position of the sugarcane fields, indicated by the red polygons ... 80 Figure 11. Landsat-8 images preprocessing flow chart. ... 112 Figure 12. Landsat-8 RBG composite (red Band04, green Band 03, blue Band02) atmospheric effect removal by LaSRC algorithm. The top corresponds to a RAW image with Cirrus Cloud, and the bottom is a Surface Reflectance image. ... 115 Figure 13 Cloudy RGB composite (top) and color scaled Quality assurance (pixel_qa) raster (bottom): the red pixels correspond to cloud, the yellow pixels correspond to cloud edge (lower confidence cloud), light blue pixels are cloud shadow. ... 117
correspond to the total images regarding the respective year. ... 118 Figure 16. Imagery selection scheme based on a correspondence threshold of 33 days. ... 119 Figure 17. Vector data preprocessing framework, represented in three main steps, and their respective tasks ... 120 Figure 18. Polygon editing procedure to complete the plot contour ... 121 Figure 19. Contours of a zone that were reassigned to individual layers. ... 121 Figure 20. Vectors reprojected to same datum as the image to avoid spatial boundaries deviation. ... 122 Figure 21. Attribute table key (left), and a common identifier (ID) to merge with another data table such as spreadsheet (right). ... 123 Figure 22. The ellipsis used to highlight the zone number in DWG files (left), was imported as polygon to the geodatabase (right). ... 124 Figure 23 High resolution RGB composite image, and a 30 m resolution color scaled NDVI image from Landsat. Landsat pixels close to field limit (red line) have distinctive color then pixel within 20 m buffer (black line). ... 125 Figure 24. Gini index graph (GREENE; MILNE, 2010). ... 126 Figure 25. Forestplot of different techniques using and datasets, and their respective MAE and 95% CI represented both graphically and numerically, estimated with Bootstrap. The vertical lines correspond to null model performance, MAE (continuous line) and CI (dashed lines).129 Figure 26. Comparison of different algorithms (Learner) using Composite and Raw datasets, using Gini index. ... 130
Table 1. Quality parameters required in the raw material of sugarcane. ... 24 Table 2. Summary of remote sensing products characteristics and respective spectral bands include in the datasets. ... 50 Table 3. Hyper-parameters tuned for each machine learning technique, with the correspondent search interval, and the respective value selected. ... 83 Table 4. Mean absolute error (MAE) and Gini coefficient of machine learning and null (Null) models, and the relative accuracy (RA) regarding mean observed value of Brix, and ... 83 Table 5. Landsat-8 imagery cloud status label description. ... 112 Table 6. Landsat-8 imagery cloud status label preview, along with cloud (white) and shadows (blue), provided by the quality assurance band. ... 112 Table 7. Available bands in Landsat-8 surface reflectance product ... 114 Table 8. Vegetation indices used in composite models. ... 127 Table 9. Hyper-parameters tuned for each machine learning technique across datasets, with correspondent search interval, and the respective value selected ... 128
1.Introduction 16
1.1.Objectives and thesis outline 17
2.remote sensing for sugarcane sugar content estimation: An operational perspective 18
2.1.Sugarcane industry 18
2.2.Sugarcane physiological development 19
2.2.1.Sugar accumulation pathways 22
2.3.Sugar content yield 23
2.4.Yield Monitoring paradigm 24
2.5.Remote sensing of vegetation 26
2.5.1.Leaf level 26
2.5.2.Canopy level response 28
2.6.Remote sensing for sugar content estimation 28
2.7.References 31
3.Feasibility of estimating sugarcane sugar content using spectral information from Landsat-8
and RapidEye remote sensing platforms: A case study 47
3.1.Introduction 47
3.2.Methods and data 48
3.2.1.Study sites 48
3.2.2.Yield dataset 49
3.2.3.Remote sensing datasets 50
3.2.4.Statistical modelling 51
3.2.5.Evaluation 53
3.3.Results and discussion 53
3.3.1.Evaluation of predictors marginal effect 53
3.3.2.Random factors marginal effect 57
3.3.3.Modeling performance 58
3.4.Conclusions 60
3.5.References 62
4.Estimating sugarcane sugar content through Landsat-8 OLI imagery: A machine learning
based transfer function 78
4.1.Introduction 78
4.2.2.1. Data Preprocessing 81
4.2.2.2. Data extraction 81
4.2.2.3. Data selection 81
4.2.3.Modeling framework 82
4.2.3.1. Hyper-parameter tuning and model training 82
4.2.3.2. Validation 83
4.3.Results and Discussion 83
4.4.Conclusions 85
4.5.References 87
5.Conclusions 110
Appendix A Remote sensing data preprocessing and extractio n 112
A.1 Raster Atmospheric Correction and Cloud Removal 112
A.2 Raster data selection 118
A.3 Raster data extraction 119
A.3.1Vector data preprocessing 119
A.3.1.1 Vector Normalization 120
A.3.1.2 Vector Transformation 122
A.3.1.3 Vector Cleaning 122
A.3.2Data Extraction 124
Appendix B Gini Coefficient 126
1. INTRODUCTION
Brazilian agro-industry is been encouraged to sustain profits by increasing production efficiency, thus ensuring continuous and sustainable production. In the case of sugarcane industry, production efficiency is possible to achieve trough effective management systems. Because of the complexity of sugarcane supply chain, sugarcane mills depend on decision support systems for planning daily operations. Management systems have evolved to a less subjective decision-making process that is backup by data, rather than decisions made from “expert wisdom”.
Sugar content estimation is imperative for the sugarcane industry as it impacts the entire production chain, therefore the provisioning of such information is critical for planning and decision-making processes. Mills still rely on field surveys to obtain sugar content information. Field surveying is time-consuming and costly, making large-scale monitoring very inefficient. Moreover, the limited intra-field accessibility leads to poor sampling representativeness, compromising the estimates due to the high degree of uncertainty. As effectiveness of data-driven decision making relies upon the quality of information gathered, thus data acquisition frameworks must be suitable for this task. A sugarcane monitoring system framework must provide reliable, timely and constant feed of information for decision makers, without sacrificing scalability.
Computer modeling strategies have been adopted as a viable solution to provide yield estimates. Models applied in sugarcane yield estimates, usually predict yield as a function of variables such as sugarcane varieties, climate and soil. A practical example is usage of crop growth models, that were originally designed to study crops physiological processes, now used in the attempt to provide accurate yield estimation. However, the spatial-temporal variability of the model parameters challenges their operational application. Moreover, the inability to account for biotic stressors is major limitations of their application. Their application is also impaired by data availability, as the acquisition of the data used as input is costly and time consuming.
Research suggests that usage of remote sensing data can be a suitable alternative to address these limitations. Remote sensing imagery have multiple attributes that are desired for crop monitoring tasks, such as the provision of information at field level in a regional scale. Another advantage is the temporal data acquisitions, allowing the continuous assessment of crop during growing season. Because of the interaction with the plant canopy, remote sensing data allows the quantitative estimation of crops biophysical parameters. Thus, spectral data
provided by remote sensing is a comprehensive and efficient way of accounting for the yield driving factors (e.g., soil, weather, sugarcane varieties), timely available, without compromising scalability.
1.1. OBJECTIVES AND THESIS OUTLINE
The scope of this research is focused on improving sugar content estimation methods, by exploring the usage of optical remote sensing images as a data source of information for modeling. As a primary objective, we aim to use sugarcane spectral response as proxy for sugar content prediction, in order to take advantage of satellite imagery capabilities of providing consistent and continuous spatial-temporal information about the observed surface. The suggested approach is based on the hypothesis that the plants response to environmental variables, is embedded in the reflectance obtained by the sensors. Since these variables also drive the plants sugar accumulation.
Chapter 2 is a literature review providing background information about sugarcane physiology, along with insights of the principles of remote sensing and the current sugarcane yield monitoring paradigm. On chapter 3 we provide a proof of concept addressing questions regarding the sugarcane spectral response according to the theory presented in the remote sensing literature. We also investigate how differences between available remote sensing systems affect their prediction performance. Chapter 4 presents a transfer function prototype developed empirically, using data derived from multispectral images and machine learning techniques, to retrieve sugar content in regional scale.
2. REMOTE SENSING FOR SUGARCANE SUGAR CONTENT ESTIMATION: AN OPERATIONAL PERSPECTIVE
2.1. SUGARCANE INDUSTRY
Traditionally used as food crop for centuries, sugarcane is increasingly being used for bioenergy production (LEAL; WALTER; SEABRA, 2013). Incentives have been created towards bioenergy, especially biofuels, due to concerns with raising oil prices, global warming and energy security (DEMIRBAS, 2009a, 2009b). Beside the reduction of petroleum dependence, biofuels have greater efficiency, due to the lower pollutant emissions when compared to fossil fuels, which directly contributes the environment protection. Other benefits such as economic opportunities, with jobs creation are also prospects for the biofuels industry (DEMIRBAS, 2009b).
Positive impact on socio-economic aspects are observed through direct and indirect jobs, and income. Literature indicates that regions were the sector is present, observed improvements in Gross National Product (GDP) and the other socio-economic indicators (MACHADO et al., 2015). When compared with overall agricultural sector, the sugarcane industry had better performance according to the observed indicators (MORAES; OLIVEIRA; DIAZ-CHAVEZ, 2015).
The Brazilian ethanol industry has increased over the years driven by increase in demands and government programs. Incentives applied by government boosted mainly the ethanol industry, through the Brazilian National Alcohol Program (PROALCOOL), considered to be largest investment in the sector, established between 1975 and 1989. The program consisted of a direct production subsidy; the Ethanol 20% blend with gasoline; launch of ethanol-only cars in 1979. Along with PROALCOOL, other indirect incentives were made through tax reduction, and low loan interest for companies willing to enter the ethanol industry (SANT’ANNA et al., 2016).
The worldwide concern with renewable sources of energy, helped to push the biofuel industry. Brazil and USA are largest ethanol producers in the world; but unlike Brazil, USA uses corn as feedstock. Despite the lower cost, sugarcane ethanol has its competitiveness impaired with corn, due transportation costs and prices of feedstocks (CRAGO et al., 2010). However, Brazil has a lot more potential of expanding sugarcane production without competing with food production. Compared to corn, sugarcane is more efficient in fuel production; obtaining 45% more ethanol in the same area (CRAGO et al., 2010). Given this factors, large uncertainties are expected in the future costs, 2030 projections shows that production is the first
driving costs up (MÉJEAN; HOPE, 2010), signaling the need of improving the sector’s efficiency.
In the recent years, the industry started looking at sugarcane as feedstock for other sources of energy (BOTHA; MOORE, 2013). Electricity generation from cane residue is increasing among the industry. The cane yields approximately 13.5% of its weight as fibrous bagasse, that is burned to produce electrical power (STANMORE, 2010). The amount the electricity produced is expressive, attending the industry’s demand, and the surplus being sold to distribution companies. According to the National Electric Power Agency – ANEEL, electricity generated from cane bagasse accounts for 6% of the national electrical energy matrix (ANEEL, 2017).
2.2. SUGARCANE PHYSIOLOGICAL DEVELOPMENT
Sugarcane is traditionally planted by vegetative propagation, asexually reproduced through stem cutting and each cut must contain at least one bud. Billet planting, with an average length of 0.4 m, usually used in mechanized planting, has 2 or 3 buds. Cutting of stems is based on the break of the effect of the apical dominance existent among the buds distributed along the stem. Normally 17 to 20 viable buds must be distributed per meter of furrow, which means that under the actual mechanized billet planting system 20 Mg ha-1 of cane should be used (BONNETT, 2013).
The growth phase of the sugarcane may be divided in four phases: initial, vegetative growth, in which growth is slow, a rapid growth phase, with the emergence and elongation of internodes, which accumulates about 75% of total dry matter, and the final phase, with the slowing down of biomass accumulation rates, sucrose concentration, a natural ripening process in the sugarcane stalks, ending with the harvest (BONNETT, 2013; INMAN-BAMBER, 2013). Sugarcane crop, as briefly mentioned above, has four different growing phases:
1) Germination (0-60 days): the sprouting phase. In this step the sprout breaks the leaves from the bud and develops straight to the soil surface, whilst increasing the root system and forming the primary shoot;
2) Tillering (60-150 days): Tiller is a shoot that sprouts, and tillering is the ability to accomplish that. First phase of stem development and stool. Tillering is a primordial characteristic of sugarcane: the main sink of the result of photosynthesis are the stalks formed from the growth of the tillers, and therefore the profitability of the crop depends primarily on the tillers produced that will dictate the final number of harvestable stalks (MATSUOKA; STOLF, 2012).
3) Development/elongation (150-240 days): This phase is characterized by intense division, differentiation, and cell elongation. Therefore, there is an increase in size and total dry plant matter. This is known as the "great period of growth". It starts slowly, grows rapidly to reach a maximum, and then decreases the amount of dry matter produced (RODRIGUES, 1995; MATSUOKA; STOLF, 2012).
4) Ripening (240-360 days): This is the maturation period characterized by the active import of sugars from the leaves. Sucrose, the principal product is accumulated in storage parenchyma of the stem (WATT; MCCORMICK; CRAMER, 2013).
The duration of the whole cycle ranges from 12 to 18 months for the cane plant, depending on the period of the year in which the cane is planted. After the cane plant harvest, a new month cycle begins, known as the ratoon cane. Basically, the ratoon cane has a 12-month cycle in a five years period (five harvests) decreasing yield after each harvest. Thereby, it is important to point out that not only the age of the crop influences the yield parameter, but other agronomic issues (cultivar, harvesting system and period, soil conditions, and climatic conditions) play an important role in crop yield as well.
Brazil has more than 500 varieties. This high variability is important because each one fits better in each cultivation condition (soil and climate). For proper classification of a cultivar regarding its agronomic potential, one should associate the knowledge of productive environments and the individual performance of the genotype. Accordingly, a cultivar may be characterized as: (a) stable, when it shows a reasonable response to most favorable growing conditions, but also shows an average performance in unfavorable conditions; (b) responsive, when showing great response in favorable growing conditions, but not well adapted to a more restrictive environment; and (c) rustic or low maintenance cultivar, which, contrary to the responsive cultivar, is most adaptable to restrictive environments, but has reduced performance in favorable growing conditions (LANDELL et al., 2010).
Considering the main goals of the sugarcane industry, which is based on sugar production and first-generation biofuels, it is very important to have both the harvest and sugarcane processing occurring at the peak of sugar concentration to maximize productivity (Scarpari & Beauclair, 2004). The ripening process, the high sugar storage peak, is a very sensitive step from the standpoint of the sugar industry, for its significant influence in the industrial phase and in the entire sugar chain. From this overview, some concepts related to physiological aspects and the ripening phase (sugar accumulation) are present.
Figure 1. Sugarcane development phases for plant-cane and ratoon cycles (CHEAVEGATTI-GIANOTTO et al., 2011).
2.2.1. SUGAR ACCUMULATION PATHWAYS
Sugarcane leaves play a central role in sucrose production (Figure 2). The sugars are synthesized by the leaves (i.e., source) trough photosynthesis and stored in the steam (i.e., sink). During source to sink translocation, the synthesized sugars are partitioned, to attend both physiological and storage demands. During growth phases, most of the sucrose produced is converted do fructose and glucose (i.e., reducing sugars) and designated to leaves and culm formation. During the maturation phase, the plant is fully developed, which generates a surplus of reducing sugars, that are converted to sucrose and stored in the stem (MOORE, 1995b; RAE et al., 2005; SACHDEVA; BHATIA; BATTA, 2011; WANG et al., 2013b; WATT; MCCORMICK; CRAMER, 2013).
Leaves have a particularly effective mechanism that converts reducing sugars into sucrose. In an experiment conducted in the laboratory, excised green leaves were supplied with a 5% glucose solution. It was observed that the concentration of sucrose in the leaves increased approximately twofold, while percentages of glucose and fructose remained at a level substantially steady, with minor fluctuations. Under field conditions, the increase of sucrose in the leaves is not observed, because it is normally translocated to the stem (HILL, 1953).
The sugar inversion process whereby sucrose is synthesized is highly influenced by the water content in the tissues. Even a small change in moisture on green leaf influences the balance. Regarding the synthetic activity reversal, another factor that influences this process is the nitrogen, leading to increased conversion efficiency (HILL, 1953). Under favorable conditions of development, the accumulation of sucrose in internodes can reach 50% of the dry weight (MOORE, 1995a). Furthermore, in some varieties, sucrose is not accumulated linearly over time, since the accumulation rate increases significantly with the maturity of internodes (MOORE, 1995c; ROHWER; BOTHA, 2001; UYS et al., 2007).
Although the described pathway of sucrose accumulation is valid in many aspects, the mechanisms that regulate sucrose translocation process in the plant, are not fully understood (SACHDEVA; BHATIA; BATTA, 2011). Despite this knowledge gap, a direct relationship between leaves and sucrose content has been well stablished, by showing a source-sink regulatory relation (WATT; MCCORMICK; CRAMER, 2013). Studies have shown that an increase on sink demands (e.g., plant growth), stimulated the photosynthesis in source leaves (MCCORMICK; CRAMER; WATT, 2008a, 2008b, 2011; INMAN-BAMBER; JACKSON; HEWITT, 2011; RIBEIRO et al., 2017). This implicates that a more developed plant will have better yield performance, not only due to higher storage capacity, but also due to an increase in sucrose synthesis capacity. Even though maturation phase is the most critical moment for
sucrose accumulation, conditions to the proper development in growth phases, will eventually result in better quality.
Figure 2. Process diagram of sucrose cycle in sugarcane (WANG et al., 2013b)
Along with endogenous factor (e.g., source-sink regulation), leaf photosynthesis is also influenced by exogenous factors such as environmental changes. Variable such as temperature conditions and water availability have a variable impact in sucrose accumulation, according to the phenological phase. Conditions of high temperature and water availability are associated with vigorous vegetative growth, and the opposite conditions favor sugarcane ripening. Depending on the condition, these factors act as promoters or inhibitors of the plants physiological processes. (CARDOZO; SENTELHAS, 2013; PEDRO MACHADO et al., 2013; SALES et al., 2013)
2.3. SUGAR CONTENT YIELD
Sugarcane sugar content is commonly used as quality index, and is a determinant factor for revenue improvement (WACLAWOVSKY et al., 2010). Sugarcane quality is determined by a set of parameters stablished by the Council of Sugarcane, Sugar and Ethanol Producers – CONSECANA (CONSECANA-SP, 2006), that dictates the production economical value. This methodology aims to equitably share risks between producers and the industry, as
the industry pays more for sugarcane with higher sugar content. Considered as fair-pricing program, it is adopted by many producers and industries.
Table 1. Quality parameters required in the raw material of sugarcane.
Quality
Parameters Description
Recommended Values POL % of sucrose in the sugarcane juice. It is the fraction of sucrose existing
in Brix
14 -24 %
BRIX % of total soluble solids in the sugarcane juice. The reading comprises all solids, in which are also sucrose, fructose, glucose etc.
18 -25 %
Purity % of sucrose in soluble solids. Indicates the degree of purity > 80 %
AR Reducing sugars. The amount of glucose and fructose in the sugarcane juice. They are precursor substances of color, increasing the color of the sugar, provoking its commercial depreciation. They are undesirable because they do not crystallize like sucrose.
< 1 %
ART Total reducing sugars. They are all the sugars of the material in the form of inverted sugars.
Variable
AT Total sugars. Sucrose + glucose + fructose, in the juice Variable
ATR Recoverable total sugars. Corresponds to the amount of sugar to be recovered per ton of cane
Variable
Unlike other crops, sugarcane maturity age is determined by the age of cane when sucrose concentration reaches a maximum value (LINGLE; IRVINE, 1994). According to the industry, the preferred condition for harvest is when sugarcane juice has high purity; in other words, when Pol % and Brix % concentration values are nearly equal. Commercially, the expected value of Pol corresponds to 16%, and purity of 80% or greater.
2.4. YIELD MONITORING PARADIGM
Since sugarcane maturity is determined by the concentration of sugar in the plant (LINGLE; IRVINE, 1994), decision makers often rely on estimations to strategically plan sugarcane production chain (LE GAL et al., 2008). Most mills still rely on field survey as the main source of information about crop status. An effective assessment of sugar accumulation, requires continuous monitoring demanding multiples surveys over time (WAGIH; ALA; MUSA, 2004), making the procedure time-consuming and costly. Another drawback is the poor sampling representativeness, since accessibility of sugarcane is very limited due sugarcane density, which increases the estimation uncertainty.
As an alternative, crop growth models originally designed to study plants’ physiological processes, have now been considered as a potential tool for crop yield prediction
(O’LEARY, 2000). However, growth models are not suitable for yield estimations at regional scales due environment heterogeneity. The spatial-temporal variability of the model parameters (e.g., soil characteristics, sugarcane variety, weather) challenges their operational application. Ideally, these model’s deployment should be restricted to land units that are considered homogenous regarding the parameters required. However, due restricted data availability this concept is not viable.
Even in a hypothetical scenario were all the inputs required by the model are available, the users must cope with several limitations attached to the model itself, such as oversimplifications. For instance, the limited number of sugarcane varieties included in the model is major restriction, since the plant response to weather conditions varies according to the genotype (CARDOZO; SENTELHAS, 2013). Research shows that sugarcane varieties show different capabilities of recovering from stress events (MACHADO et al., 2009). Moreover, the inability to account for biotic stressors (e.g., pests and diseases), is a strong indication that growth models do not represent field conditions appropriately, and are likely to produce different estimations compared to field reality (VALADE et al., 2014; O. RAUFF; BELLO, 2015).
Conversely, models developed empirically allows sugarcane yield estimations trough a more simplistic framework. Compared to growth models, the key advantage of empirical models is the possibility incorporate any variable in a more straightforward manner, allowing for instance the insertion of sugarcane cultivars information. Multiples studies that adopted this approach to estimate sugarcane yield have shown promising results (BOCCA; RODRIGUES, 2016; EVERINGHAM et al., 2016; OLIVEIRA; BOCCA; RODRIGUES, 2017). Since empirical models rely on the data used in their construction to make predictions, the data acquisition process becomes problematic. Soil parameters for instance, are costly and time-consuming. Besides the cost, delays in data acquisition makes the application of the model unfeasible.
Alternatively, remote sensing systems are valuable tools that allows the assessment of crop biophysical parameters, providing continuous data acquisition in global scale, and great consistency. Furthermore, the possibility of obtaining data at no cost, is a major advantage of this approach. The potential of remote sensing system for crop parameters assessment, is presented in the following sections.
2.5. REMOTE SENSING OF VEGETATION
Remote sensing tools have the capabilities of estimating various biophysical (e.g., canopy structure and volume) and biochemical properties (foliar chlorophyll and lignin) of vegetation. Even though direct measurements of such features are more accurate, methods based on remote sensing are preferred because they are faster and non-destructive, therefore more suitable for continuous large-scale monitoring. The basis of vegetation parameters retrieval trough remote sensing relies on interaction of electromagnetic radiation with plants leaves (e.g., absorbance, reflectance, transmittance, and scattering). Vegetation reflected radiation, carries information related to its properties, thus the analysis of the spectral response allows the estimation of multiple features with reasonable accuracy (GOEL, 1988; ASNER, 1997; OLLINGER, 2011).
Vegetation spectral response is acquired through optical sensors, commonly recording radiation reflected within the range of 0.4 to 2.5 μm, encompassing the Visible (VIS), Near Infrared (NIR), and Shortwave Infrared (SWIR) regions. Many studies based on experimental and modeling evidence, have shown that leaf optical response in this region is a function of the foliar chemistry, structure, water content (GOEL, 1988; JACQUEMOUD et al., 1996; ASNER, 1997; OLLINGER, 2011; MOUSIVAND et al., 2015).
2.5.1. LEAF LEVEL
Figure 3 illustrates sugarcane leaf spectral profile, and the interaction of radiation with multiple leaf compounds. The graphic shows that the reflectance variates according to region of the spectra observed, and the leaf chemical and structural properties. For each region of the spectra, there is a dominant factor influencing the response.
Figure 3. Leaf spectral reflectance main dominant factors (adapted from (PETERSON et al., 1988; ADAM; MUTANGA; RUGEGE, 2010). The spectral curve corresponds to the reflectance of a mature (green) and senescent (dry) sugarcane leaf, measured by a contact probe for single leaf.
For the VIS region (0.4 – 0.7 µm), the reflectance is lower due to high absorption due to leaf pigments. Wavelength corresponding to the Blue (0.45 µm) and Red (0.67 µm) regions, are more strongly absorbed by the Chlorophylls “a” and “b”, when compare to Green (0.55 µm) region (GAUSMAN; ALLEN; CARDENAS, 1969; JACQUEMOUD et al., 1996; CARTER; KNAPP, 2001). For instance, leaf pigment change due to senescence changes the VIS absorption. Not only fresh leaves differentiate spectrally from dry leaves, but also fresh leaves in different developmental phases.
In the NIR region (0.7 – 1.3 µm), energy absorbance is lower than any other portion of the spectra. Since there is no significative interaction between chlorophyll and NIR wavelengths, the most dominant factor is the leaf tissue structures. The radiation is scattered by air spaces in the spongy mesophyll, reflecting most of the incident radiation (PETERSON et al., 1988). The SWIR (1.3 – 2.5 µm) is mostly influenced by leaf water content, characterized by strong absorption of radiation (CARTER, 1991). The curve denotes peaks of absorption in bands centered at 1.45 µm, 1.95 µm and 2.5 µm. This effects are caused by water itself present
in the atmosphere, and water in the tissue of plant, that variates with hydration and water stress (OLLINGER, 2011).
Although, these leaf-related factors are predominating in vegetation spectral response, when the sensor acquire data from the whole-canopy, other factors must be considered in the observed reflectance (PETERSON et al., 1988).
2.5.2. CANOPY LEVEL RESPONSE
Sugarcane canopy is a key component of sugarcane development, it’s structure affects directly the sunlight interception and use efficiency; thus also affecting the sugarcane yield (LUO et al., 2013). Composed by leaves that vary in size, shape and orientation, the canopy is characterized quantitatively through a set of parameters such as leaf area index, leaf distributions, leaf clumping and inclination angle; as they influence the in amount of radiation intercepted, and transmitted to lower portions of the canopy (OLLINGER, 2011; LUO et al., 2013; MARCHIORI; MACHADO; RIBEIRO, 2014). Therefore, along with biochemical composition of leaves, the canopy properties have also a direct impact on sugarcane yield, since it governs light interception (ASNER, 1997; SMIT; SINGELS, 2006; CASTRO-NAVA et al., 2016).
Remote sensing captures the variations in canopy structure, as it directly affects the reflectance of the plant. Leaf distribution and inclination interfere in the directional distribution of photon scattering, producing a different reflectance response for each canopy structural conformation. For instance, leaves with more vertically oriented permit better penetration of incident light, and consequently reducing whole-canopy reflectance (ASNER, 1997; OLLINGER, 2011). Therefore, since canopy reflectance can be related to the interaction between plant and incident radiation, we can consider the its structural variation as a valuable indicator of sugarcane yield.
2.6. REMOTE SENSING FOR SUGAR CONTENT ESTIMATION
The detection of sugar content accumulatio requires constant monitoring, stablishing a demand for simple and effective frameworks (TERAUCHI; IREI; TERAJIMA, 2012). Remote sensing based vegetation monitoring, has been studied extensively for several decades for periodic assessment of changes in crop growth and development.
Numerous studies have reported methods that utilize near infrared spectroscopy to predict sucrose in the extracted sugarcane juice (TEWARI; IRUDAYARAJ, 2003;
VALDERRAMA; BRAGA; POPPI, 2007; SOROL et al., 2010; NAWI; JENSEN; CHEN, 2012; MAT NAWI et al., 2013; NAWI; CHEN; JENSEN, 2014). Infrared spectroscopy is a suitable alternative for conventional methods used in the laboratories, that required the use of chemical reagents (potentially toxic), and time-consuming, rendering to be impractical (MELQUIADES et al., 2012). Although spectroscopy does not require chemical reagents, this method requires preparation procedures, such as cutting, milling, pressing, clarification, for further analysis on a saccharimeter or optical analysis equipment using radiation wavelength near infrared (NIR). Regarding the procedures required, this method still struggle when it comes to large volume of samples.
As an alternative for indoors methods, MAT NAWI et al., (2013) showed that field spectrometry of the cane stalk can be applied to estimate sugar yield. Reflectance data of the stalk were correlated with the Brix of sugarcane through regression analysis by partial least squares (PLS – Partial Least Squares) and artificial neural networks (ANN – Artificial Neural Network). The model obtained by PLS resulted in coefficient of determination (R²) of 0.91, and the accuracy obtained by ANN ranged between 50% and 100%. Nevertheless, this method is not very effective because it is restricted ground-based measurements, which increases the cost and time of surveys.
Sensors on board of aerial or space platforms are more suitable for large scale surveys, however data acquisition is limited to the canopy of the plant, which presumes the existence of a relationship between the canopy reflectance and the plant sugar content. Attempts to estimate sugar content form leaf spectral response show divergent results. In a research conducted by ZHAO et al., (2012), the spectral response of in situ measurements on 87 different sugarcane genotypes was used to estimate the chemical composition of the leaves (chlorophyll, N, and C), and cane yield. The results obtained from measured and estimated values of chlorophyll, N, and C had ratios of 0.543 and 0.824 (p<0.0001) for all variables. Notwithstanding, parameters related to productivity (sucrose, recoverable sugar, tons of sucrose per hectare) showed no significant correlation (p<0.01).
JOHNSON and RICHARD, (2011) used plant cane and ratoon crops of sugarcane with multiple cultivars to determine if leaf reflectance measurements could be used to predict sugar content levels. They found that leaf reflectance was effective for predicting sugar content in 56 to 79% of the cases when cultivars were combined using resubstitution, and in 36 to 54% of the cases using cross validation. When cultivars were considered separately, the prediction accuracy was much higher (99 to 100% for resubstitution and 60-100% in cross validation). They concluded that regression analyses between leaf reflectance values and sugar content
indicated that simple models of leaf reflectance could be developed to describe much of the variability in stalk sucrose levels (JOHNSON; RICHARD, 2011).
Most of the studies relating sucrose content with the optical properties of sugarcane, rely on ground-based measurements. Sugarcane fields have poor accessibility due to cane height and density, which may lead to poor sampling representativeness, making this type of approach inappropriate for large areas. In view of these factors, multispectral images derived from spaceborne sensors, provide the means for spatio-temporal variation assessment of large areas. Multiple studies showed the potential of satellite images for sugarcane monitoring, including precision agriculture (qualitative assessment in real time) and territorial planning (assessment and regional production surveying).
APAN et al., (2004) reported that development of canopy reflectance data acquired by the Earth Observing-1 (EO-1) Hyperion hyperspectral imagery could detect sugarcane orange rust disease caused by Puccinia kuehnii in Australia. In Brazil, (GALVÃO; FORMAGGIO; TISOT, 2005, 2006) and in United States, (JOHNSON; VIATOR, 2008) used EO-1 Hyperion data to discriminate sugarcane genotypes. Besides it’s lower performance when compared to hyperspectral sensors (GALVÃO; FORMAGGIO; TISOT, 2006), sensors with coarser spectral and spatial resolutions, present other characteristics essential for crop monitoring, such as reasonable temporal availability and global coverage. Despite been a valuable source of information for sugarcane biophysical parameter retrieval, remote sensing images have yet not been directly applied on the sugar content assessments.
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