Abstract Purpose Grain quality is typically assessed during the reception and shipment stages at storage and processing units through physical classification. However, traditional methods based on visual inspection are often subjective, imprecise, and time-consuming. This study aimed to evaluate the use of near-infrared spectroscopy (NIRS) and hyperspectral sensors combined with machine learning algorithms to determine the physicochemical properties and classify the quality of white, parboiled, black, and red rice. Methods and Results Spectral data were acquired using a FieldSpec 4 Jr spectroradiometer covering the 350–2500-nm range, with analysis focused on the 350–750-nm and 1400–2100-nm intervals. These spectral windows correspond to the most informative absorption features associated with O–H, C–H, and N–H bonds, directly related to the moisture, protein, starch, and lipid contents of rice grains. Samples were classified according to regulatory standards, and representative samples were subdivided into 100 sub-samples of 20 g each. Distinct nutritional and physicochemical profiles were observed among rice types. Principal component analysis (PCA) effectively discriminated compositional characteristics, emphasizing the higher nutritional value of pigmented rice. Hyperspectral signatures revealed distinct spectral differences among rice types based on their physicochemical composition. The combination of NIRS, hyperspectral sensors, and machine learning algorithms achieved high accuracy across all evaluation metrics, with the J48, SL, RF, and SVM models delivering the best performance in rice quality classification. Conclusion The integration of NIR spectroscopy, hyperspectral sensing, and machine learning models provides a rapid, non-destructive, and highly accurate approach for assessing the physicochemical quality of rice in storage and processing facilities. This methodology demonstrates strong potential for enhancing efficiency, reproducibility, and objectivity in grain quality monitoring, offering a data-driven alternative to traditional visual inspection methods. Graphical Abstract

Abstract Purpose This study explores the efficacy of microwaves as a method for the control of three weed species particularly widespread and problematic in the Italian farms cultivating IV range crops. These findings, based on laboratory experiments, are part of a project (MOPAS Project—Microwaves for Sustainable Agriculture) meant to realize a prototype for use in real field situations. Methods Seeds from weed species ( Trifolium repens L., Portulaca oleracea L., and Solanum nigrum L.) were subjected to microwave treatments at 480 and 900 W power levels for 10 and 30 s. The impact on seed germination and ion leakage was assessed. Histological changes induced by microwaves were also examined through microscopic analysis. Results Significant germinability decreases were observed in S. nigrum and T. repens with increasing treatment duration and intensity. P. oleracea exhibited lower sensitivity, with detrimental effects only at the highest power levels suggesting that seed size and the associated differences in water absorption may play a crucial role in determining sensitivity to microwave exposure. S. nigrum membrane stability was affected only at the highest irradiation power, while all species showed increased ion leakage across treatments. Observations revealed wilting of stems and leaves, indicating cell turgidity loss in inner tissue layers. Conclusion Microwave radiation may be a non-chemical weed control strategy, though efficacy is influenced by variability in seed traits. Further research is necessary to optimize treatment parameters and assess scalability under field conditions. This approach holds potential for reducing herbicide use and promoting sustainable agricultural practices.