The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.

Maize seed variety classification has become essential in agriculture, driven by advancements in non-destructive sensing and machine learning techniques. This study introduced an efficient method for maize variety identification by combining hyperspectral imaging with a framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Spectral data were acquired by hyperspectral imaging technology from five maize varieties and processed using Savitzky–Golay (SG) smoothing, along with standard normal variate (SNV) preprocessing. To enhance feature selection, the competitive adaptive reweighted sampling (CARS) algorithm was applied to reduce redundant information, identifying 100 key wavelengths from an initial set of 774. This method successfully minimized data dimensionality, reduced variable collinearity, and boosted the model’s stability and computational efficiency. A CNN-LSTM model, built on the selected wavelengths, achieved an accuracy of 95.27% in maize variety classification, outperforming traditional chemometric models like partial least squares discriminant analysis, support vector machines, and extreme learning machines. These results showed that the CNN-LSTM model excelled in extracting complex spectral features and offering strong generalization and classification capabilities. Therefore, the model proposed in this study served as an effective tool for maize variety identification.

Ultraviolet radiation is increasingly used as a controllable physical technology for crop disease management, but inconsistencies in parameter reporting, the validation scale, and engineering evidence hinder cross-study comparisons and practical translation. Following the PRISMA 2020 statement, this systematic review searched the Web of Science Core Collection and IEEE Xplore, screened eligible studies, and conducted full-text data extraction and structured coding. A total of 80 studies were evaluated across four domains: UV treatment parameter reporting, highest validation level, translational maturity, and engineering characteristics of application equipment. The treatment frequency or number of applications and wavelength were explicitly reported in 83.8% and 76.3% of studies, respectively, whereas irradiance and post-treatment light or dark conditions were reported in only 36.3% and 35.0%. In 57.5% of studies, the highest validation level remained limited to in vitro pathogen assays, detached-material experiments, or controlled-environment whole-plant experiments; low and low-medium translational maturity together accounted for 62.5%. Among the 11 equipment-related studies included in this review, the operating speed and target-surface dose calibration were reported more frequently than within-canopy dose uniformity, shading effects, and operational control. UV-based crop disease control is progressing toward field and equipment applications, but incomplete parameter reporting, discontinuities in the production-scale evidence chain, and unstable dose delivery still constrain reproducibility and scale-up. Future research should establish minimum reporting standards, strengthen multi-site and multi-season validation, translate biologically effective doses into equipment operating parameters, and develop closed-loop control based on actual target-surface dose monitoring.