Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied.

This review explores the transformative potential of precision agriculture and proximal sensing in revolutionizing crop management practices. By delving into the complexities of these cutting-edge technologies, it examines their role in mitigating the adverse impacts of agrochemical usage while bringing crop health monitoring to a high precision level. The review explains how precision agriculture optimizes production while safeguarding environmental integrity, thus offering a viable solution to both ecological and economic challenges arising from excessive agrochemical application. Furthermore, it investigates various proximal sensing techniques, including spectral imaging, thermal imaging, and fluorescence sensors, showcasing their efficacy in detecting and diagnosing crop health indicators such as stress factors, nutrient deficiencies, diseases, and pests. Through an in-depth analysis of relevant studies and successful practical applications, this review highlights that it is essential to bridge the gap between monitoring sensors and real-time decision-making and to improve image processing and data management systems to fully realize their potential in terms of sustainable crop management practices.

This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R²P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R²P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R² = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling.

Multispectral imaging plays a key role in crop monitoring. A major challenge, however, is spectral band misalignment, which can hinder accurate plant health assessment by distorting the calculation of vegetation indices. This study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images. By using multispectral camera lenses as binocular pairs, the sensor acquisition distance was estimated, and an alignment model was developed for distances ranging from 500 mm to 1500 mm. The approach relied on selecting the red band image as a reference, while the remaining bands were treated as moving images. The stereo camera calibration algorithm estimated the target distance, enabling the correction of band misalignment through previously developed models. The alignment models were applied to assess the health status of baby leaf crops (Lactuca sativa cv. Maverik) by analyzing spectral indices correlated with chlorophyll content. The results showed that the stereo vision approach used for distance estimation achieved high accuracy, with average reprojection errors of approximately 0.013 pixels (4.485 × 10⁻⁵ mm). Additionally, the proposed linear model was able to explain reasonably the effect of distance on alignment offsets. The overall performance of the proposed experimental alignment models was satisfactory, with offset errors on the bands less than 3 pixels. Despite the results being not yet sufficiently robust for a fully predictive model of chlorophyll content in plants, the analysis of vegetation indices demonstrated a clear distinction between healthy and unhealthy plants.

Drosophila suzukii (Matsumura), also known as spotted wing drosophila (SWD), is invasive, with a preference for infesting commercially viable soft berries, particularly cherries. SWD infestations in sweet cherries are difficult to detect and remove in the field, packing houses, and processing lines, causing significant economic losses and reducing yields significantly, necessitating early detection of insect infestation in fruits during primary decaying stages. Few publications have addressed the use of non-destructive techniques for the detection of insect infestation in cherries. Based on the advantages and effectiveness of the spectrophotometric techniques, an attempt was made to use the spectrophotometry to rapidly detect postharvest SWD infestations of intact sweet cherry fruit, to employ it in sweet cherry fruit selection and grading processes. The main purpose of this study was to apply spectrophotometry as a rapid and non-destructive method in detecting and classifying healthy sweet cherry fruit versus that infested with SWD eggs. To model the data fit/prediction, principal components regression and partial least squares regression algorithms were considered. The external cross-validation set was initially set to 20% of the overall available samples and subsequently increased to 50% in the final selected optimal model. The identified procedure of management of regression algorithms allowed the selection of a very performant and robust model using the partial least squares regression algorithm: its false negative rate and false positive rate, after 500 Monte Carlo runs, were 0.004% +/− 0.003 and 0.02% +/− 0.01, respectively, and, in addition, the 50% of samples were used for the external cross-validation set.