Saltanat Baidalina
Saltanat Baidalina has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Akhama Akhet
Department of Agriculture and Bioresources, Shokan Ualikhanov Kokshetau University, 76 Abay str., Kokshetau 020000, Kazakhstan
2 shared publications - Ildar Bogapov
Department of Agriculture and Bioresources, Shokan Ualikhanov Kokshetau University, 76 Abay str., Kokshetau 020000, Kazakhstan
2 shared publications - Marden Baidalin
Department of Agriculture and Bioresources, Shokan Ualikhanov Kokshetau University, 76 Abay str., Kokshetau 020000, Kazakhstan
2 shared publications - Zhanat Salikova
Department of Agriculture and Bioresources, Shokan Ualikhanov Kokshetau University, 76 Abay str., Kokshetau 020000, Kazakhstan
2 shared publications - Abylaikhan Myrzakhanov
Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan
1 shared publication - Huseyin Atakan Varol
Institute of Smart Systems and Artificial Intelligence (ISSAI), Nazarbayev University, Astana 010000, Kazakhstan
1 shared publication
Latest Publications
- Open Access
- 11 May 2026
- Published by MDPI AG in Preprints.org
Evaluation of Sugar Sorghum Genotypes for Bioethanol Production
Sugar sorghum has emerged as a highly promising crop for bioethanol production owing to its high biomass yield potential and its remarkable capacity to accumulate fermentable sugars in the stalks. The aim of this study was to evaluate the productivity, sugar accumulation capacity, and bioethanol potential of 12 sugar sorghum accessions under the conditions of Northern Kazakhstan. Field experiments were carried out over the 2024-2025 growing seasons, during which key agronomic and technological traits were assessed, including green biomass yield, juice yield, total soluble solids (Brix), sugar concentration, and theoretical ethanol yield. The analysis of variance revealed that biomass yield was predominantly driven by weather conditions (p < 0.001), whereas sugar concentration was significantly influenced by genotype (p < 0.05). Several genotypes, namely Volonter, Kapital, Sevilya, Flagman, Chayka, and Sauri, consistently exhibited high sugar productivity and bioethanol potential across years, confirming considerable genetic variability in these traits. Storage of stem juice resulted in sugar losses of up to 30.7%, indicating the necessity for rapid processing of raw biomass after harvest. Under laboratory fermentation conditions, juice from the Sevilya genotype (17.9 Brix) achieved a sugar-to-ethanol conversion efficiency of 74.3% relative to the theoretical yield. Overall, the findings confirm the suitability of sugar sorghum for bioethanol production and highlight its strong potential as an energy crop under the arid agroclimatic conditions of Northern Kazakhstan.
- 11 May 2026
- Published by MDPI AG in Preprints.org
Open AccessEvaluation of Sugar Sorghum Genotypes for Bioethanol Production
Sugar sorghum has emerged as a highly promising crop for bioethanol production owing to its high biomass yield potential and its remarkable capacity to accumulate fermentable sugars in the stalks. The aim of this study was to evaluate the productivity, sugar accumulation capacity, and bioethanol potential of 12 sugar sorghum accessions under the conditions of Northern Kazakhstan. Field experiments were carried out over the 2024-2025 growing seasons, during which key agronomic and technological traits were assessed, including green biomass yield, juice yield, total soluble solids (Brix), sugar concentration, and theoretical ethanol yield. The analysis of variance revealed that biomass yield was predominantly driven by weather conditions (p < 0.001), whereas sugar concentration was significantly influenced by genotype (p < 0.05). Several genotypes, namely Volonter, Kapital, Sevilya, Flagman, Chayka, and Sauri, consistently exhibited high sugar productivity and bioethanol potential across years, confirming considerable genetic variability in these traits. Storage of stem juice resulted in sugar losses of up to 30.7%, indicating the necessity for rapid processing of raw biomass after harvest. Under laboratory fermentation conditions, juice from the Sevilya genotype (17.9 Brix) achieved a sugar-to-ethanol conversion efficiency of 74.3% relative to the theoretical yield. Overall, the findings confirm the suitability of sugar sorghum for bioethanol production and highlight its strong potential as an energy crop under the arid agroclimatic conditions of Northern Kazakhstan.
- Open Access
AI-Powered Aerial Multispectral Imaging for Forage Crop Maturity Assessment: A Case Study in Northern Kazakhstan
Forage crops play a vital role in ensuring livestock productivity and food security in Northern Kazakhstan, a region characterized by highly variable weather conditions. However, traditional methods for assessing crop maturity remain time-consuming and labor-intensive, underscoring the need for automated monitoring solutions. Recent advances in remote sensing and artificial intelligence (AI) offer new opportunities to address this challenge. In this study, unmanned aerial vehicle (UAV)-based multispectral imaging was used to monitor the development of forage crops—pea, sudangrass, common vetch, oat—and their mixtures under field conditions in Northern Kazakhstan. A multispectral dataset consisting of five spectral bands was collected and processed to generate vegetation indices. Using a ResNet-based neural network model, the study achieved a high predictive accuracy ( = 0.985) for estimating the continuous maturity index. The trained model was further integrated into a web-based platform to enable real-time visualization and analysis, providing a practical tool for automated crop maturity assessment and long-term agricultural monitoring.Open AccessAI-Powered Aerial Multispectral Imaging for Forage Crop Maturity Assessment: A Case Study in Northern Kazakhstan
Forage crops play a vital role in ensuring livestock productivity and food security in Northern Kazakhstan, a region characterized by highly variable weather conditions. However, traditional methods for assessing crop maturity remain time-consuming and labor-intensive, underscoring the need for automated monitoring solutions. Recent advances in remote sensing and artificial intelligence (AI) offer new opportunities to address this challenge. In this study, unmanned aerial vehicle (UAV)-based multispectral imaging was used to monitor the development of forage crops—pea, sudangrass, common vetch, oat—and their mixtures under field conditions in Northern Kazakhstan. A multispectral dataset consisting of five spectral bands was collected and processed to generate vegetation indices. Using a ResNet-based neural network model, the study achieved a high predictive accuracy ( = 0.985) for estimating the continuous maturity index. The trained model was further integrated into a web-based platform to enable real-time visualization and analysis, providing a practical tool for automated crop maturity assessment and long-term agricultural monitoring.