Analysis of Arbuscular Mycorrhizal Fungal Community in Soil of Sugarcane at Different Tillage Depths - Qun-Qing Huang; Sheng Wang; Ying-Jie Nong; Kai-Mao Zhong; Yi-Cheng Su; Xiao-Juan Qin; Jin-Lian Zhang; Yi-Hao Kang; Ting-Su Chen; Shi-Yuan Zeng; Xi-Chao Deng; Ming-Guo Jiang; et al. (Sugar Tech, 2026)

Accelerating Sugarcane Breeding with Machine Learning: A Concurrent Multi-trait Predictive Approach - Haichuan Fan; Jing Chao; Xiaoyan Liu; Pengcheng Ma; Guanghu Zhu; Ming Li; Rui Yan; Xinyi Li; Yan Jing; Fengbing Li; Ting Luo; Prakash Lakshmanan (Sugar Tech, 2026)
A large and highly polyploid genome, long breeding cycles, low narrow-sense heritability of yield and its components, and persisting yield stagnation continue to challenge sugarcane variety improvement through both conventional and molecular breedings. To address this, this study developed an efficient phenotype-based concurrent multi-trait prediction framework using artificial intelligence, leveraging 2700 parent combinations and their offspring field performance data from the Sugarcane Research Institute of Guangxi Academy of Agricultural Sciences. Five machine learning paradigms were selected based on data characteristics: tree-based models (GBDT, XGBoost) for structured tabular data and nonlinear trait interactions; attention-based tabular models (TabTransformer, TabNet) for subtle cross-trait dependencies; and an MLP as a baseline. All models were optimized via 80/20 stratified train–test split, class-weighted loss, and cross-validated grid search. GBDT achieved the best overall performance with a macro F 1 score of 0.7316, Hamming loss of 0.2052, and sample accuracy of 0.5630, excelling in predicting sucrose content, cane yield, and smut resistance. XGBoost performed comparably, while TabTransformer offered complementary advantages with a higher F 1 score (0.6829) on overall agronomic performance. These results indicate the potential of using artificial intelligence (AI), particularly machine learning (ML), to increase the predictive power and robustness through integrated AI approaches in the future. This phenotype-based framework shows the value of ML as an efficient decision support tool that can help breeders prioritize cross combinations and optimize resource use, thereby reducing reliance on extensive field trials. While the potential of AI in accelerating variety development is evident, its routine application in real-world commercial breeding requires validation.

Quantitative Proteomics Analysis of Sugarcane Ratoon Crop Chlorosis - Ye-Geng Fan; Rong-Fa Chen; Li-Hang Qiu; Zhong-Feng Zhou; Hui-Wen Zhou; Ji-Guang Wei; Xiu-Peng Song; Ting Luo; Prakash Lakshmanan; Xing Huang; Jian-Ming Wu; Yang-Rui Li (Sugar Tech, 2021)

Intrusion of Fall Armyworm (Spodoptera frugiperda) in Sugarcane and Its Control by Drone in China - Xiu-Peng Song; Yong-Jian Liang; Xiao-Qiu Zhang; Zhen-Qiang Qin; Jin-Ju Wei; Yang-Rui Li; Jian-Ming Wu (Sugar Tech, 2020)

Effects of Spray Parameters of Drone on the Droplet Deposition in Sugarcane Canopy - Xiao-Qiu Zhang; Xiu-Peng Song; Yong-Jian Liang; Zhen-Qiang Qin; Bao-Qing Zhang; Jin-Ju Wei; Yang-Rui Li; Jian-Ming Wu (Sugar Tech, 2020)

Effects of Exogenous GA3 and DPC Treatments on Levels of Endogenous Hormone and Expression of Key Gibberellin Biosynthesis Pathway Genes During Stem Elongation in Sugarcane - Li-Hang Qiu; Rong-Fa Chen; Han-Min Luo; Ye-Geng Fan; Xing Huang; Jun-Xian Liu; Fa-Qian Xiong; Hui-Wen Zhou; Chong-Kun Gan; Jian-Ming Wu; Yang-Rui Li (Sugar Tech, 2019)

Cloning and Identification of Differentially Expressed Genes Associated with Smut in Sugarcane - Xiu-Peng Song; Dan-Dan Tian; Ming-Hui Chen; Zhen-Qiang Qin; Jin-Ju Wei; Chun-Yan Wei; Xiao-Qiu Zhang; De-Wei Li; Li-Tao Yang; Yang-Rui Li (Sugar Tech, 2018)

Nutrient Decomposition Rate and Sugarcane Yield as Influenced by Mung Bean Intercropping and Crop Residue Recycling - Tie-Guang He; Li-Rong Su; Yang-Rui Li; Tian-Ming Su; Fang Qin; Qin Li (Sugar Tech, 2018)

Sugar Industry and Improved Sugarcane Farming Technologies in China - Yang-Rui Li; Xiu-Peng Song; Jian-Ming Wu; Chang-Ning Li; Qiang Liang; Xi-Hui Liu; Wei-Zan Wang; Hong-Wei Tan; Li-Tao Yang (Sugar Tech, 2016)

Effects of Exogenous Abscisic Acid on Cell Membrane and Endogenous Hormone Contents in Leaves of Sugarcane Seedlings under Cold Stress - Xing Huang; Ming-Hui Chen; Li-Tao Yang; Yang-Rui Li; Jian-Ming Wu (Sugar Tech, 2015)

Molecular Cloning and Functional Analysis of an Ethylene Receptor Gene from Sugarcane (Saccharum spp.) by Hormone and Environmental Stresses - Ai-Qin Wang; Xing-Zhi Ye; Jing-Li Huang; Jun-Qi Niu; Ming Liu; Yong-Bao Pan; Li-Tao Yang; Yang-Rui Li (Sugar Tech, 2015)

Effects of Ethephon on Key Enzymes of Sucrose Metabolism in Relation to Sucrose Accumulation in Sugarcane - Ai-Qin Wang; Wen-Jing Huang; Jun-Qi Niu; Ming Liu; Li-Tao Yang; Yang-Rui Li (Sugar Tech, 2013)

Effect of Long-Term Vinasse Application on Physico-chemical Properties of Sugarcane Field Soils - Ze-Pu Jiang; Yang-Rui Li; Guang-Po Wei; Qing Liao; Tian-Ming Su; Yan-Cheng Meng; Hai-Ying Zhang; Chang-Yan Lu (Sugar Tech, 2012)

Macronutrients Absorption and Surface Runoff Losses Under Different Fertilizer Treatments in Sugarcane Field - Tian-Ming Su; Yang-Rui Li; Guang-Po Wei; Ze-Pu Jiang; Qing Liao; Shu-Biao Zhu (Sugar Tech, 2012)