Evaluation of Remote Sensing and Meteorological parameters for Yield Prediction of Sugarcane (Saccharum officinarum L.) Crop

Saini, Preeti; Nagpal, Bharti; Garg, Puneet; Kumar, Sachin

doi:10.1590/1678-4324-2023220781

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/rs16050863/s1, Table S1: Basic statistics on RMSE (ton ha −1 ) of the selected papers' models, where DM means Data Mining; Table S2: Attributes based on field information; Table S3: Attributes based on spectral bands and vegetation indices; Table S4: Attributes based on meteorological data; Table S5: Attributes based on SAR data; Table S6: Attributes based on terrain information; Table S7: Other attribute types [111][112][113][114][115][116].…”

Section: Supplementary Materialsmentioning

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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Section: Supplementary Materialsmentioning

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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Forecast of Students’ Mental Health Combining an Artificial Intelligence Technique and Fuzzy Inference System

Ramasamy,

Khan,

Joghee

et al. 2024

2024 International Conference on Automation and Computation (AUTOCOM)

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Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model

Zhou,

Pan,

Guan

et al. 2023

Agriculture

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As a raw material for sugar, ethanol, and energy, sugarcane plays an important role in China’s strategic material reserves, economic development, and energy production. To guarantee the sustainable growth of the sugarcane industry and boost sustainable energy reserves, it is imperative to forecast the yield in the primary sugarcane production regions. However, due to environmental differences caused by regional differences and changeable climate, the accuracy of traditional models is generally low. In this study, we counted the environmental information and yield of the main sugarcane-producing areas in the past 15 years, adopted the LSSVM algorithm to construct the environmental information and sugarcane yield model, and combined it with WCA to optimize the parameters of LSSVM. To verify the validity of the proposed model, WCA-LSSVM is applied to two instances based on temporal differences and geographical differences and compared with other models. The results show that the accuracy of the WCA-LSSVM model is much better than that of other yield prediction models. The RMSE of the two instances are 5.385 ton/ha and 5.032 ton/ha, respectively, accounting for 7.65% and 6.92% of the average yield. And the other evaluation indicators MAE, R2, MAPE, and SMAPE are also ahead of the other models to varying degrees. We also conducted a sensitivity analysis of environmental variables at different growth stages of sugarcane and found that in addition to the main influencing factors (temperature and precipitation), soil humidity at different depths had a significant impact on crop yield. In conclusion, this study presents a highly precise model for predicting sugarcane yield, a useful tool for planning sugarcane production, enhancing yield, and advancing the field of agricultural production prediction.

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Evaluation of Remote Sensing and Meteorological parameters for Yield Prediction of Sugarcane (Saccharum officinarum L.) Crop

Cited by 3 publications

References 21 publications

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

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

Forecast of Students’ Mental Health Combining an Artificial Intelligence Technique and Fuzzy Inference System

Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model

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