Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

Reducing postharvest losses offers a significant opportunity to enhance food availability without requiring extra production resources. A substantial portion of cereal grain goes to waste annually due to a lack of science-based knowledge, unconscious handling practices, suboptimal technical efficiency, and inadequate infrastructure. This article extensively reviews losses occurring during postharvest operations across various crops, examining diverse postharvest operations in different countries. Recent advancements in postharvest technology research are thoroughly discussed. The primary obstacles and challenges hindering the adoption and implementation of postharvest technologies are also explored. The appropriate postharvest technology relies on specific factors, including the kind of crops, production locales, seasons, and existing environmental and socioeconomic conditions.

Micro-/nanoplastic contamination in agricultural soils raises concerns on agroecosystems and poses potential health risks. Some of agricultural soils have received significant amounts of micro-/nanoplastics (MNPs) through plastic mulch film and biosolid applications. However, a comprehensive understanding of the MNP impacts on soils and plants remains elusive. The interaction between soil particles and MNPs is an extremely complex issue due to the different properties and heterogeneity of soils and the diverse characteristics of MNPs. Moreover, MNPs are a class of relatively new anthropogenic pollutants that may negatively affect plants and food. Herein, we presented a comprehensive review of the impacts of MNPs on the properties of soil and the growth of plants. We also discussed different strategies for mitigating or eliminating MNP contamination. Moreover, perspectives for future research on MNP contamination in the agricultural soils are also highlighted.

In this study, prediction and analysis of energy and exergy in a combined hot air-infrared dryer with ultrasound pretreatment for organic blackberry was carried out. The effect on product color and greenhouse gas (GHG) emission was assessed. To predict energy and exergy parameters such as energy utilization ratio, energy utilization, exergy loss, and exergy efficiency, both the artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) methods were employed. Drying experiments were undertaken at three temperature levels of 50, 60, and 70 °C in air speed of 1 m/s and ultrasound pretreatment time 15, 30, and 45 min, as compared to controlled samples (without pretreatment). Results demonstrated that by raising the inlet air temperature and ultrasound pretreatment time, color change rate decreased, while energy utilization and exergy efficiency increased. Energy and exergy prediction results by means of ANN and ANFIS methods showed that ANFIS method achieved a higher _R2 and lower _RMS as compared to ANN. The highest level of GHG emission (NO_x, CO₂) was obtained at 50 °C temperature for samples without pretreatment.