A recent study investigates the role of sonic vibrations in improving pollination efficiency and fruit size in tomatoes. 

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).