Citrus spp. are spread mainly in the Mediterranean basin and represent the largest fruit source for human consumption. Postharvest losses, mainly due to diseases and metabolic disorders of fruits, can cause severe wastage, reaching 30 to 50% of the total production. Preserving quality and extending shelf life are essential objectives for postharvest technological innovation, determined by the proper handling, treatment, storage and transport of harvested produce. Moreover, the application of novel sustainable strategies is critical for the reduction of synthetic fungicide residues on fruit surfaces and the impact on the environment caused by waste disposal of fungicides. In this article, the current knowledge about the safest and more sustainable strategies, as well as advanced postharvest handling and storage technologies, will be critically reviewed.

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.

The marketing value of table grapes is contingent upon several quality requirements, mostly related to microbial decay, sugar/acidity ratio, and colour. This research explores the impact of combining organic-cultured compatible techniques to delay disorders along with organic grape distribution in post-harvest. Aurebasidum pullulans in-field application on grape bunches at three growing stages as a biocontrol agent against grey mould growth coupled with massive modified atmosphere packaging (MMAP; 20% CO₂, 10% O₂) equipped with a breathable valve was tested. The in-field treatment had a significant impact on the colour and sugar content of the grapes at harvest and the mould count evolution during storage, whilst the trend of the other parameters was mainly affected by the interaction of the variables tested. The untreated batch experienced the worst behaviour and the packaging was paramount in preserving the moisture content and appearance of the bunches. The findings of this study may contribute to developing novel practices for setting a smart distribution of organic table grapes and reducing food waste.