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.

Plant diseases can greatly affect the total production of food and agricultural materials, which may lead to high amount of losses in terms of quality, quantity and also in economic sense. To reduce the losses due to plant diseases, early diseases detection either based on a visual inspection or laboratory tests are widely employed. However, these techniques are labor-intensive and time consuming. In a view to overcome the shortcoming of these conventional approaches, several researchers have developed non-invasive techniques. Recently, spectroscopy technique has become one of the most available non-invasive methods utilized in detecting plant diseases. However, most of the studies on the application of this novel technology are still in the experimental stages, and are carried out in isolation with no comprehensive information on the most suitable approach. This problem could affect the advancement and commercialization of spectroscopy technology in early plant disease detection. Here, we review the applications and limitations of spectroscopy techniques (visible/infrared, electrical impedance and fluorescence spectroscopy) in early detection of plant disease. Particular emphasis was given to different spectral level, challenges and future outlook.