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.

Virgin coconut oil is a new highly value added version of coconut oil in health food markets. Extraction of virgin coconut oil from coconut kernel is a major influential step for their commercialization. There are many extraction methods, among which cold and hot extraction are conventional extraction processes. The hot extractions carried out by pressing the clean, ground and fresh coconut, to yield coconut milk followed by heating at high temperature that could remove the useful micronutrient. In cold process, extraction of coconut oil takes place through destabilization of coconut milk emulsion without heating such as fermentation, chilling and thawing, or centrifugation, enzymatic treatment. Hot extraction processes has been provide better yield than cold extraction processes. In cold extraction processes there are various methods MOJ Food Processing & Technology

The present work aimed to optimize the extraction of milk and oil from coconut by an aqueous enzymatic extraction process using the enzyme Viscozyme-L. The combine effect of grinding time (24min), amount of Viscozyme-L (60-180FBGU) and incubation time (2-7h), were optimized using Response Surface Methodology. The selected method provides the best yield of coconut milk (73.88± 0.5%, w/w) and Virgin Coconut Oil (21.57± 0.4%, w/w) with percentage recovery of oil was 86.14% of fat present in the fresh coconut kernel at grinding time (3min), the amount of enzyme (120FBGU) and incubation time (4.5h). The experimental results were significantly (P< 0.05) comparable to the predicted yield of coconut milk (73.33%) and oil (21.12%). A validation model that performed within the selected range of variation confirmed these results, microscopic image of cell wall and particle distribution in coconut milk, suggested that the use of the Viscozyme-L treatment could enhance the extraction of oil.