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Agriculture is an important component of the concept of sustainable development. Given the projected population growth, sustainable agriculture must accomplish food security while also being economically viable, socially responsible, and having the least possible impact on biodiversity and natural ecosystems. Deep learning has shown to be a sophisticated approach for big data analysis, with several successful cases in image processing, object identification, and other domains. It has lately been applied in food science and engineering. Among the issues and concerns addressed by these systems were food recognition; quality detection of fruits, vegetables, meat, and aquatic items; food supply chain; and food contamination. In precision agriculture, Artificial Intelligence (AI) is a commonly used technology for estimating food quality. It is especially important when evaluating crops at different phases of harvest and postharvest. Crop disease and damage detection is a high-priority activity because some postharvest diseases or damages, such as decay, can destroy crops and produce poisons that are toxic to humans. In this paper, we use Convolutional Neural Networks (CNNs)-based U-Net, DeepLab, and Mask R-CNN models to detect and predict postharvest deterioration zones in stored apple fruits. Our approach is unique in that it segmented and predicted postharvest decay and nondecay zones in fruits separately. This review will focus on postharvest physiology and management of fruits and vegetables, including harvesting, handling, packing, storage, and hygiene, to reduce postharvest loss (PHL) and improve crop quality. It will also cover postharvest handling under extreme weather conditions and potential impacts of climate change on vegetable postharvest and postharvest biotechnology on PHL.

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The interaction between a freely rising thermal plume and an unheated vertical surface in its neighbourhood has been investigated. The underlying transport mechanisms are of interest from a fundamental standpoint, as well as in a variety of practical problems, such as the cooling of electronic equipment and room fires. A detailed numerical and experimental study of the flow is carried out. The temperature and velocity gradients are expected to be large, particularly near the thermal source. Also, any constraints imposed on the entrainment into the flow in the vicinity of the source are expected to significantly affect the nature of the flow and the interaction. These considerations make it imperative to solve the full governing equations in the interaction region. These equations are solved numerically by finite-difference methods, employing the vorticity-stream function formulation. The important physical variables in the problem are the total thermal energy input by the source, the size of the source, and the distance of the source from the vertical wall which is taken as adiabatic or isothermal in the computation. The flow is found to be strongly deflected towards the vertical surface for the parametric ranges considered. As expected, the diffusion effects in the main flow direction are found to decay downstream and the flow to gradually approach the characteristics of a wall plume resulting from a concentrated line heat source with the same total heat input. Thus, the axial diffusion terms may be neglected far downstream, allowing the flow there to be approximated as a boundary, layer, with the full equations being solved in the interaction region. Finally, an experimental investigation is carried out to characterize the nature of the interaction. The flow is visualized by means of a shadowgraph and the temperature field is measured in the interaction region, downstream of the source. Numerical predictions agree with the experimental results, lending support to the numerical model for this interaction.

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Understanding the behavior of an aeroelastic system beyond the critical point is essential for effective implementation of any active control scheme since the control system design depends on the type of instability (bifurcation) the system encounters. Previous studies had found the aeroelastic system to enter into chaos beyond the point of instability. In the present work, an attempt has been made to carry out an experimental study on an aeroelastic model placed in a wind tunnel, to understand the behavior of aerodynamics around a wing section undergoing classical flutter. Wind speed was increased from zero until the model encountered flutter. Pressure at various locations along the surface of wing and acceleration at multiple points on the wing were measured in real time for the entire duration of experiment. A Leading Edge Separation Bubble (LSB) was observed beyond the critical point. The growing strength of the LSB with increasing wind speed was found to alter the aerodynamic moment acting on the system, which forced the system to enter into a second bifurcation. Based on the nature of the response, the system appears to undergo periodic doubling bifurcation rather than Hopf-bifurcation, resulting in chaotic motion. Eliminating the LSB can help in preventing the system from entering chaos. Any active flow control scheme that can avoid or counter the formation of leading edge separation bubble can be a potential solution to control the classical flutter.