Sugar Tech最新文献

{"title":"Spatial Modeling of Sugarcane Yield Using Machine Learning Approaches","authors":"Diego A. Socadagui-Casas,&nbsp;Andres F. Rodriguez-Vasquez,&nbsp;Yolanda Rubiano-Sanabria,&nbsp;Daniel M. Jimenez-Gutierrez,&nbsp;Sonia R. Socadagui-Casas","doi":"10.1007/s12355-026-01736-0","DOIUrl":"10.1007/s12355-026-01736-0","url":null,"abstract":"<div><p>Sustainable precision agriculture and artificial intelligence (AI) are transforming modern farming by enhancing resource efficiency, addressing climate challenges, and fostering sustainability. In Colombia, sugarcane cultivation is pivotal in the agricultural economy, particularly in the Cauca River Valley, a region grappling with climate variability and resource optimization challenges. This study introduces an innovative methodology that combines machine learning (ML) techniques with geospatial data to predict sugarcane yields accurately. The research employs the CatBoost, random forest, and XGBoost ML algorithms, integrating historical data on climate, soil characteristics, and agricultural management practices to develop spatially detailed predictive models. The findings demonstrate that ML-based approaches outperform traditional methods like linear and penalized regressions, offering more precise agricultural planning and resource management predictions. Focused on the municipality of La Candelaria, this work underscores the potential of ML technologies to enhance productivity while promoting sustainable agriculture.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"675 - 685"},"PeriodicalIF":2.0,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12355-026-01736-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the Modeling Method of the Lodging Period Sugarcane Root-Soil System Based on SPH–FEM Coupling Method","authors":"Wang Yang,&nbsp;Xinkai Dang,&nbsp;Zhiheng Lu,&nbsp;Kuo Liao,&nbsp;Chengni Pang,&nbsp;Weilong Dai","doi":"10.1007/s12355-026-01748-w","DOIUrl":"10.1007/s12355-026-01748-w","url":null,"abstract":"<div><p>Sugarcane lodging severely hampers mechanized harvesting. Existing research primarily focuses on agronomy, with limited investigation into the root-soil interaction mechanisms, which constrains the optimization of lodging resistance technologies. Numerical simulation offers a key approach for studying this problem. However, suitable modeling methods for the sugarcane root-soil system remain scarce. In this study, we first employ a SPH-FEM coupling method combined with physical experiments to propose a parameter-equivalence approach for root materials, thereby constructing a simulation model of the root-soil system during the sugarcane lodging period. Subsequently, through simulation experiment design, regression analysis, and parameter optimization, a mathematical relationship is established between the cross-sectional height dimension <i>h</i>_<i>y</i> of the fibrous roots and simulation time, as well as the simulation accuracy. The results indicate that the SPH-FEM coupled model for the root-soil system during the lodging phase is applicable to sugarcane lodging research. The optimal equivalence method for root elastic modulus is based on tensile deformation equivalence. As <i>h</i>_<i>y</i> increases, the simulation time first decreases rapidly and then levels off, while the simulation error increases slowly initially and then rise more sharply. Optimization yields a height of 6 mm, corresponding to the following material parameters: density 129.397 kg m⁻³, elastic modulus 110.927 MPa, and static and dynamic friction coefficients against the soil of 0.195 and 0.189, respectively. The proposed modeling method provides critical technical support for studying crop lodging and complex root-soil systems and facilitating in-depth research into crop-soil interaction mechanisms.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"712 - 725"},"PeriodicalIF":2.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Site-Specific UAV Spraying Versus Conventional Weed Control in Sugarcane: Effects on Herbicide Use, Efficiency, and Environmental Impact","authors":"Lalita Panduangnat,&nbsp;Jetsada Posom,&nbsp;Santimaitree Gonkhamdee,&nbsp;Arthit Phuphaphud,&nbsp;Kanda Saikaew,&nbsp;Abdul Momin,&nbsp;Okayasu Takashi,&nbsp;Khwantri Saengprachatanarug","doi":"10.1007/s12355-026-01742-2","DOIUrl":"10.1007/s12355-026-01742-2","url":null,"abstract":"<div><p>Effective weed management is essential to maximize crop yield and reduce environmental impacts. Conventional weed control methods often lead to broad-spectrum herbicide applications, increasing both costs and environmental impact. This study investigated the effectiveness and resource efficiency of various weed control methods in sugarcane cultivation, including boom sprayer, drone sprayer, drone site-specific sprayer (SS UAV), knapsack sprayer, and hand-weeding. The results indicate that the SS UAV provides the highest level of environmental sustainability, achieving a weed control efficiency (WCE) of 94.35% while reducing water and herbicide use. Specifically, this method reduced herbicide usage by up to 64% and emissions by a factor of 7.63 compared to the boom sprayer. In larger fields with lower weed densities, the SS UAV demonstrated superior operational efficiency (ha/h) because of its ability to precisely target weed-infested areas. A cost analysis further suggested that the SS UAV could reduce the overall spraying costs by up to 44.30% compared to traditional methods. These results highlight the potential for large-scale adoption in precision agriculture, demonstrating that this method is more environmentally friendly, cost-effective, and efficient in terms of labor, water, and chemical usage.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"686 - 699"},"PeriodicalIF":2.0,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Sugar Yield in Sugarcane Using Green Normalized Difference Vegetation Index Derived from Imagery Obtained by Remotely Piloted Aircrafts","authors":"Julio Cezar Souza Vasconcelos,&nbsp;Caio Simplicio Arantes,&nbsp;Eduardo Antonio Speranza,&nbsp;João Francisco Gonçalves Antunes,&nbsp;Luiz Antonio Falaguasta Barbosa,&nbsp;Geraldo Magela de Almeida Cançado","doi":"10.1007/s12355-026-01737-z","DOIUrl":"10.1007/s12355-026-01737-z","url":null,"abstract":"<div><p>Sugarcane (<i>Saccharum officinarum</i> L.) is one of the largest crops in Brazil, and its productivity varies according to the environment and management practices adopted. In this study, tons of sugar per hectare (TSH) are estimated using a heteroscedastic gamma (GA) regression model, which considers several explanatory variables, one of which is the normalized difference green vegetation index (GNDVI), obtained from multispectral images in two locations over two consecutive growing seasons. The modeling considers regression structures in the parameters representing the mean and coefficient of variation, respectively. The results show that there is an influence of location, cultivar, cycle, accumulated precipitation, and GNDVI. To verify if the model is well-fitted to the data, the analysis of quantile residuals shows that the model is adequate. Therefore, the results indicate that heteroscedastic GA regression is an alternative model for predicting TSH and can assist in decision-making in sugarcane cultivation.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"667 - 674"},"PeriodicalIF":2.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12355-026-01737-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Critical Review of Agribots and Intelligent Systems for Sugar Crop Health and Farmer Advisory","authors":"Sudipta Mahato,&nbsp;Ritwik Sahoo,&nbsp;Dipankar Barai,&nbsp;Waghamare Minal Bhujangrao,&nbsp;Sumit Sow,&nbsp;Shivani Ranjan,&nbsp;Subir Dutta,&nbsp;Dibyendu Ghosh","doi":"10.1007/s12355-026-01738-y","DOIUrl":"10.1007/s12355-026-01738-y","url":null,"abstract":"<div><p>Sugar-yielding crops such as sugarcane (<i>Saccharum spp.</i>), sugar beet (<i>Beta vulgaris</i>), and sweet sorghum (<i>Sorghum bicolor</i>) are vital to the global agro-industrial economy, yet their productivity is increasingly constrained by diverse biotic and abiotic stressors. This review synthesizes recent advances in artificial intelligence (AI), robotics, and the Internet of Things (IoT) that are transforming sugar crop health monitoring, disease diagnostics, and decision support. Deep-learning algorithms and convolutional neural networks (CNNs) have achieved diagnostic accuracies exceeding 95% for major diseases, while unmanned aerial vehicles (UAVs) and multispectral imaging enable high-resolution, spatiotemporal assessment of crop health. Agribots designed for autonomous scouting, precision weeding, nutrient optimization, and mechanized harvesting enhance field efficiency, reduce dependence on manual labor, and lower environmental footprints. IoT-enabled sensor arrays continuously monitor soil, canopy, and microclimatic parameters, facilitating predictive analytics for irrigation, nutrient management, and stress forecasting. Emerging paradigms—such as generative and regenerative AI, blockchain-based traceability, and 5G-edge computing—further strengthen system scalability, data integrity, and operational responsiveness. Despite challenges related to implementation cost, data interoperability, and digital literacy gaps, these convergent technologies collectively offer a transformative framework for precision-oriented, climate-resilient, and sustainable sugar agriculture. The proposed integrative Agribot–IoT–AI model establishes a closed-loop advisory system linking real-time sensing, predictive modeling, and farmer interaction, supporting sustainable intensification and bio-economic resilience.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"390 - 408"},"PeriodicalIF":2.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Application of a Method to Enhance Lipase Performance and Wastewater Treatment Capacity Using DES","authors":"Jiaxin Song,&nbsp;Chuanlong Yu,&nbsp;Fangling Wu,&nbsp;Li Yuan,&nbsp;Danna Fan,&nbsp;Yiheng Zhang,&nbsp;Yuping You,&nbsp;Guoqiang Wu,&nbsp;Zedong Zhang,&nbsp;Wenjun Wang","doi":"10.1007/s12355-026-01741-3","DOIUrl":"10.1007/s12355-026-01741-3","url":null,"abstract":"<div><p>Enzymatic catalysis has made significant breakthroughs in the decolorization of molasses wastewater. However, challenges such as the difficulty in recovering free enzymes hinder the application of this technology on an industrial scale and limit its development. This study introduces a low deep eutectic solvent-assisted immobilization technique (DES-IM) to explore the potential application of lipase in the decolorization of molasses wastewater. The results have demonstrated that immobilized lipase (Lecitase® Ultra) retained 53.29% of its initial activity after 6 h of continuous operation at elevated temperatures. Furthermore, under strong acidic and alkaline conditions, it maintained residual activities of 68.17% and 72.30%, respectively; this activity under extreme conditions significantly exceeded that of IM-Ultra. Additionally, experiments conducted with industrial-grade molasses wastewater indicated that DES-IM-Ultra improved decolorization efficiency by 12.03% compared to IM-Ultra, achieving an impressive decolorization efficiency of 87.48%. In conclusion, this technology demonstrates strong reproducibility, high stability, and effective decolorization. It offered a more environmentally friendly and efficient strategy for the industrial decolorization of molasses wastewater.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"700 - 711"},"PeriodicalIF":2.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Machine Learning and Early-Stage Screening to Evaluate Genotype-Specific Seedling Responses to Drought in Sugar Beets (Beta vulgaris L.)","authors":"Omar Gaoua,&nbsp;Mehmet Arslan","doi":"10.1007/s12355-026-01728-0","DOIUrl":"10.1007/s12355-026-01728-0","url":null,"abstract":"<div><p>Sugar beet (<i>Beta vulgaris</i> L.) is a significant source of refined sugar, and its yield heavily depends on successful germination and early seedling establishment. Drought during these stages hampers growth and stand vigor, making genotype-specific evaluation under early water deficit essential for breeding stress-resilient cultivars. In this study, six sugar beet genotypes were evaluated across four polyethylene glycol (PEG) concentrations (0%, 5%, 9%, and 12%) to simulate mild to severe water deficit. Normal seedling count (NS) and early seedling growth traits (root length, shoot length, root and shoot fresh and dry weights) were recorded. Five machine learning algorithms, extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), and Gaussian process (GP), were applied to model and describe within-experiment relationships between PEG-induced stress intensity, genotype, and early seedling trait variation. PEG-induced stress resulted in significant decreases in all measured traits, with notable genotype-dependent variation. Among the tested algorithms, RF and MLP showed the highest within-dataset modeling performance (<i>R</i>² ≈ 0.81–0.76), followed closely by XGBoost. In contrast, the kernel-based models GP and SVM achieved moderate performance. Genotype PI590669 exhibited comparatively stronger early seedling performance under severe PEG stress, whereas PI590855 was more sensitive. This study highlights the value of combining physiological traits with machine learning-based modeling to support comparative evaluation of genotype responses under controlled drought conditions. By facilitating a multivariate comparison of genotype responses under PEG-induced drought, this approach provides a framework for the efficient and consistent identification of early-stage stress responses under conditions relevant to the increasing frequency of drought associated with climate change. Future research should extend these methods to multi-environment evaluations, later developmental stages, and integrate genomic data to assess the broader applicability of these findings.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"650 - 666"},"PeriodicalIF":2.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Powered Sugar Industry: Transforming Production, Processing and Sustainability","authors":"R. John Martin,&nbsp;Bhushan Gosavi,&nbsp;Bhushankumar Nemade,&nbsp;Varucha Misra","doi":"10.1007/s12355-026-01722-6","DOIUrl":"10.1007/s12355-026-01722-6","url":null,"abstract":"","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"361 - 363"},"PeriodicalIF":2.0,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning Approach for Detecting Mineral Trash in Sugarcane Processing Through Turbidity Measurements","authors":"Julián David Montes,&nbsp;Maria Alejandra Gomez,&nbsp;Roosembert Polanco,&nbsp;Ana Milena Escobar,&nbsp;Nicolas Gil Zapata","doi":"10.1007/s12355-025-01705-z","DOIUrl":"10.1007/s12355-025-01705-z","url":null,"abstract":"<div><p>The efficiency of sugar mill is significantly impacted by the level of mineral trash entering with the cane. This type of trash adversely affects milling equipment, raises ash content in bagasse, and diminishes clarification and filtration effectiveness due to higher mud levels, impacting extraction, cogeneration, and sucrose recovery. These effects have intensified with the increased mechanical harvest, from 40% in 2013 to 75% in 2023. Combined with climate variability and high precipitation events, this has led to mineral trash levels occasionally exceeding 5%, when normal levels are between 0.8 to 1.0%. An exploratory study was carried out to estimate of mineral trash content by turbidity measurements in mixed juice, aiming to provide real-time data for timely interventions. At laboratory scale, mineral impact was simulated by doping clean juices from the stalks press with soil, which showed a strong correlation (R2 &gt; 0.90) between turbidity and mineral trash, above other variables evaluated such as juice deterioration and color variations due to varietal differences. Based on these findings, an analysis of historical data recorded during nearly two years by a sugar mill was conducted, evaluating variables such as turbidity and insoluble solids in mixed juice, ash in bagasse, and filter cake as a percentage of cane. Clustering techniques were used, and relationships were identified between turbidity and these variables of interest. Finally, a K-nearest neighbors (KNN) classification model was trained, achieving an accuracy above 75%, allowing the classification of mineral trash levels into low, medium, and high categories. This model could be implemented online with the installation of a turbidity sensor in mixed juice, enabling timely process interventions.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"364 - 374"},"PeriodicalIF":2.0,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Innovations in Sugarcane Farming and Sugar Industry in India: Enhancing Productivity, Efficiency, and Sustainability","authors":"Varucha Misra,&nbsp;B. Gosavi,&nbsp;Govind P. Rao,&nbsp;R. Viswanathan,&nbsp;S. Solomon","doi":"10.1007/s12355-025-01715-x","DOIUrl":"10.1007/s12355-025-01715-x","url":null,"abstract":"<div><p>The sugar industry supports over 50 million farmers and their families, contributing significantly to India’s economy with over INR 1200 billion in sugar and alcohol production. However, the industry faces challenges like climate change, water scarcity, and pest infestations. Artificial Intelligence (AI) offers solutions through precision technology, data-driven decision-making, and sustainable practices. AI can boost productivity and profitability through satellite-based tracking, AI-driven irrigation, predictive analytics, and robotics. Despite potential, AI adoption in the Indian sugar industry remains fragmented, and research gaps exist in developing scalable, farmer-friendly systems and climate-resilient models. Addressing these issues can position AI to drive sustainable growth, resilience, and competitiveness of the sugar industry sector in India. An updated progress of AI applications in the Indian sugar industry, along with constraints and future potentials, is discussed.</p></div>","PeriodicalId":781,"journal":{"name":"Sugar Tech","volume":"28 :","pages":"375 - 389"},"PeriodicalIF":2.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}