Qing Wei and colleagues from the College of Engineering, China Agricultural University, systematically elaborated on the innovative applications of neural networks in agricultural product drying, offering new insights to address industry pain points. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025620).

A review by Professor Abdul Shukor JURAIMI’s team from Universiti Putra Malaysia points out that hyperspectral imaging technology boasts advantages of non-contact operation, high precision, and early detection. Compared with traditional manual visual inspection, it can complete detection within 10–30 days after rice sowing—a critical period when weeds are most competitive—with an identification accuracy generally exceeding 90%. For example, regarding Echinochloa crus-galli and weedy rice (Oryza sativa f. spontanea), the most common weeds in rice fields, researchers achieved identification accuracies of 100% and 92%, respectively, by analyzing spectral data with intelligent algorithms. This accurate identification lays the foundation for targeted weeding: combined with UAVs and prescription mapping technology, it enables site-specific herbicide application, reducing pesticide usage by up to 50%. This not only cuts costs but also alleviates environmental burdens. The relevant article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025619).

The sorghum salt tolerant gene SbTEF1 was identified by genome-wide association study (GWAS) and the PAV284 which located in the promoter region of SbTEF1 was the key regulatory locus governing the salt tolerant ability.

An international research collaboration, including INRAE, has published the complete set of genes (pangenome) and agronomic traits (panphenome) of the eggplant. Beyond the genome, this comprehensive collection encompasses all known genetic variations within the species, including those involved in traits such as prickle development. Drawing on a global collection of more than 3,400 cultivated and wild eggplant varieties, the researchers identified over 20,000 gene families and 218 agronomic traits, including resistance to fungal wilt and antioxidant capacity. The dataset is freely accessible and provides valuable resources for breeders seeking to develop customised varieties adapted to local conditions and ongoing climate change. The results have been published in Nature Communications.