Machine learning model ensemble for predicting sugarcane yield through synergy of optical and SAR remote sensing

Das, Ayan; Kumar, Mukesh; Kushwaha, Amit; Dave, Rucha; Dakhore, K. K.; Chaudhari, K. N.; Bhattacharya, Bimal K.

doi:10.1016/j.rsase.2023.100962

Cited by 5 publications

(4 citation statements)

References 46 publications

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“…In terms of digitalization in sugarcane production and automatic intervention, several studies have been conducted (Figure 5), mostly related to sensing and predictive approaches, for example, biomass [67][68][69], gaps [1,3,53,[70][71][72][73][74], lodging identification and classification [75], yield estimation [76][77][78][79][80][81][82], nitrogen application [69,83], sugarcane disease detection [84,85], weed control [52,86], improved cropland use [87], harvesting planning [88,89], and prediction of seed replenishment positions [90]. [68], improved cropland use [87], image classifier [52], gaps [73], lodging identification [75] a yield estimation [79].…”

Section: Digital Solutions In Sugarcane Mechanizationmentioning

confidence: 99%

Challenges of Digital Solutions in Sugarcane Crop Production: A Review

Molin,

Wei,

da Silva

2024

AgriEngineering

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Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communication among different hardware and datasets, improving system sustainability. Sugarcane mechanization has some particularities that mandate the development of custom solutions based on digital tools, which are being applied globally in different crops. Digital mechanization can be conceived as the application of digital tools on mechanical operation. This review paper addresses different digital solutions that have contributed towards the mechanization of sugarcane crop production. The process of digitalization and transformation in agriculture and its related operations to sugarcane are presented, highlighting important ICT applications such as real-time mechanical operations monitoring and integration among operations, demonstrating their contributions and limitations regarding management efficiency. In addition, this article presents the major challenges to overcome and possible guidance on research to address these issues, i.e., poor communication technologies available, need for more focus on field and crop data, and lack of data interoperability among mechanized systems.

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Section: Digital Solutions In Sugarcane Mechanizationmentioning

confidence: 99%

Challenges of Digital Solutions in Sugarcane Crop Production: A Review

Molin,

Wei,

da Silva

2024

AgriEngineering

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“…Ref. [80] integrated RS data from Sentinel-1 and Sentinel-2 and different machine learning models to estimate sugarcane yield at field level in India, presenting Normalized Root Mean Square Errors (NRMSE, %) of 18% and 32%. Ref.…”

Section: Attributes Used In the Selected Papers That Made Use Of Stat...mentioning

confidence: 99%

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

de França e Silva,

Chaves,

Luciano

et al. 2024

Remote Sensing

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The sugarcane crop has great socioeconomic relevance because of its use in the production of sugar, bioelectricity, and ethanol. Mainly cultivated in tropical and subtropical countries, such as Brazil, India, and China, this crop presented a global harvested area of 17.4 million hectares (Mha) in 2021. Thus, decision making in this activity needs reliable information. Obtaining accurate sugarcane yield estimates is challenging, and in this sense, it is important to reduce uncertainties. Currently, it can be estimated by empirical or mechanistic approaches. However, the model’s peculiarities vary according to the availability of data and the spatial scale. Here, we present a systematic review to discuss state-of-the-art sugarcane yield estimation approaches using remote sensing and crop simulation models. We consulted 1398 papers, and we focused on 72 of them, published between January 2017 and June 2023 in the main scientific databases (e.g., AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We observed how the models vary in space and time, presenting the potential, challenges, limitations, and outlooks for enhancing decision making in the sugarcane crop supply chain. We concluded that remote sensing data assimilation both in mechanistic and empirical models is promising and will be enhanced in the coming years, due to the increasing availability of free Earth observation data.

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“…Moreover, the application of remotely sensed data extends beyond spatial identification, encompassing diverse domains such as crop type characterization [33,34], yield prediction [35,36], estimation of biophysical parameters [37,38], nutritional requirements [39], and disease detection [40].…”

Section: Introductionmentioning

confidence: 99%

In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy

Lodato,

Pennazza,

Santonico

et al. 2024

Remote Sensing

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The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system.

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Machine learning model ensemble for predicting sugarcane yield through synergy of optical and SAR remote sensing

Cited by 5 publications

References 46 publications

Challenges of Digital Solutions in Sugarcane Crop Production: A Review

Challenges of Digital Solutions in Sugarcane Crop Production: A Review

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy

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