Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods

Nihar, Ashmitha; Patel, N. R.; Pokhariyal, Shweta; Danodia, Abhishek

doi:10.1007/s12524-021-01444-0

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…We can train an ML algorithm on a heterogenous and “messy” dataset to learn meaningful and non-duplicative patterns to solve a task automatically, accurately, and unbiasedly. Some applications of ML for sugarcane research and development available from earlier independent studies include predicting or forecasting chlorophyll content ( Narmilan et al., 2022 ), standard morphophysiological variables ( Oliveira et al., 2022 ), production of biomass ( Wang et al., 2022 ), and classify cultivation ( Nihar et al., 2022 ). We developed a new pathway by mapping spectral features to °Brix and Purity; hence we can fulfill a gap in analyzing qualitative yield while improving the addressability of a UAV for scalable aerial remote sensing.…”

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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“…Out of the total studies included in the sub-category of crop classification (Table 7), maximum studies were based on optical dataset [40-50], while six studies [51][52][53][54][55][56] utilized the capability of both optical and microwave remote sensing to classify crops, particularly during Kharif season. Refs.…”

Section: Crop Classificationmentioning

confidence: 99%

Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

Pokhariyal,

Patel,

Govind

2023

Agronomy

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In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management.

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“…Crop sequence classification uses a flowchartlike hybrid structure experienced bee which is integrated in GIS. Nihar et al (2022) describe use of multi-date Sentinel-2 data to discriminate and map sugarcane fields in a study area in Saharanpur district of Uttar Pradesh state. Classification approach adopted random forest and SVM and successful separation of ratoon and planted fields was demonstrated.…”

Section: Crop Discrimination and Mappingmentioning

confidence: 99%

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

Ray

Dadhwal

Navalgund

2022

J Indian Soc Remote Sens

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The paper traces the evolution of agricultural remote sensing in India through its four phases: initial exploratory and aerial data based (1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982), IRS Utilization Program led (1983( -1995( ), post-IRS-1C launch (1996( -2011 and since the establishment of a dedicated institution for EO-based crop forecasting and other agricultural applications (2012 onward). Published field-scale studies are discussed before introducing the submissions to the special issue of the journal, namely Advanced Geospatial Technologies for Agriculture with Special emphasis on field level Applications. A total of 17 articles grouped under crop discrimination and mapping, crop yield estimation, crop suitability analysis, irrigation management and others (UAV, fodder and field boundary extraction) and 15 of these describe work over India and remaining two have Iran as their study area. These studies are linked to other published work as well as challenges under major issues of data access, use of UAV, use of smartphones, data processing and crop simulation models. Taken together, these articles report substantial improvements in capabilities of field-scale monitoring for developing new class of agricultural applications in India as well as other regions in the world with small farm holdings.

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Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods

Cited by 12 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

Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

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