Machine-Learning-Based Regional Yield Forecasting for Sugarcane Crop in Uttar Pradesh, India

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

doi:10.1007/s12524-022-01549-0

Cited by 20 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational advances that lead to the use of machine learning and deep learning algorithms have expanded the development of agricultural crop yield models using empirical approaches and RS data [13,21]. Different strategies have been used to obtain sugarcane yield using empirical models, such as Linear Regression, Multiple Linear Regression, and Stepwise Multiple Regression [11,[22][23][24][25], Support Vector Machine (SVM) [11,18,26,27], Artificial Neural Networks (ANN) [11,28,29], and Random Forest (RF) [12,18,22,26,27,[30][31][32]. As input, they use RS, field, agrometeorological, and terrain data, among others.…”

Section: Empirical Modelsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Empirical Modelsmentioning

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

View full text Add to dashboard Cite

show abstract

“…Time-series yield information for Uttar Pradesh's individual districts was culled from the states. The agricultural mask for the research region was extracted using the land-use land cover (50k) map available through thematic services on the Bhuvan Portal (Indian Geo-Platform of ISRO) [21].…”

Section: Dataset Descriptionmentioning

confidence: 99%

Sugarcane yield prediction using NOA-based swin transformer model in IoT smart agriculture

Krishnan,

Rao,

Prasad

et al. 2023

J App Biol Biotech

View full text Add to dashboard Cite

The Internet of things (IoT) empowers precise organization and intelligent coordination for industrial facilities and smart farming, enhancing agricultural efficiency. Sugar production relies on various auxiliary elements, but in laborintensive smart agriculture, creating accurate forecasts is a formidable challenge. Machine learning emerges as a potential solution, as current convolutional neural network-based phase recognition techniques struggle with longrange dependencies. To address this, a temporal-based swin transformer network (TSTN) is introduced, comprising a swin transformer and long short-term memory (LSTM). The swin transformer employs attention mechanisms for expressive representations, while LSTM excels at extracting temporal data with long-range dependencies. The nutcracker optimizer algorithm (NOA) fine-tunes LSTM weights. TSTN effectively blends these components, providing spatiotemporal data with enhanced context. This model outperforms competitors in accuracy, as demonstrated through testing with data from Uttar Pradesh. The integration of IoT and TSTN marks a significant advancement in optimizing agricultural operations for increased productivity and efficiency. In the comparative analysis, the proposed TSTN-NOA model achieves better performance and results than other existing models. ARTICLE HIGHLIGHTSSugarcane Yield prediction is carried out by Temporal-based Swin Transformer Network (TSTN), because existing CNN models are not very adequate for the extraction of long-range dependencies. The Swin Transformer incorporates the attention mechanism to encode remote dependencies and learn highly expressive representations. The proposed model is focused on Uttar Pradesh mainly because sugarcane was cultivated on 28.53 lakh hectares in Uttar Pradesh, which is more than any state in the country. To improve the classification accuracy, the Nutcracker optimizer algorithm is used for selecting the weight of the Long Short-Term Memory that is one of the components in the TSTN model.

show abstract

“…However, this varied by location. When inputs were lowered in regions with big phosphorus surpluses and easy-to-access soil, the region's yearly phosphorus budget revealed plenty of potential for improvement [8,9].…”

Section: Related Workmentioning

confidence: 99%

Hybrid CNN-SVM Classifier Approaches to Process Semi-Structured Data in Sugarcane Yield Forecasting Production

et al. 2023

View full text Add to dashboard Cite

Information communication technology (ICT) breakthroughs have boosted global social and economic progress. Most rural Indians rely on agriculture for income. The growing population requires modern agricultural practices. ICT is crucial for educating farmers on how to be environmentally friendly. It helps them create more food by solving a variety of challenges. India’s sugarcane crop is popular and lucrative. Long-term crops that require water do not need specific soil. They need water; the ground should always have adequate water due to the link between cane growth and evaporation. This research focuses on forecasting soil moisture and classifying sugarcane output; sugarcane has so many applications that it must be categorized. This research examines these claims: The first phase model predicts soil moisture using two-level ensemble classifiers. Secondly, to boost performance, the proposed ensemble model integrates the Gaussian probabilistic method (GPM), the convolutional neural network (CNN), and support vector machines (SVM). The suggested approach aims to correctly anticipate future soil moisture measurements affecting crop growth and cultivation. The proposed model is 89.53% more accurate than conventional neural network classifiers. The recommended models’ outcomes will assist farmers and agricultural authorities in boosting production.

show abstract

Machine-Learning-Based Regional Yield Forecasting for Sugarcane Crop in Uttar Pradesh, India

Cited by 20 publications

References 34 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

Sugarcane yield prediction using NOA-based swin transformer model in IoT smart agriculture

Hybrid CNN-SVM Classifier Approaches to Process Semi-Structured Data in Sugarcane Yield Forecasting Production

Contact Info

Product

Resources

About