Recent Advancement in Postharvest Loss Mitigation and Quality Management of Fruits and Vegetables Using Machine Learning Frameworks

Abstract

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.

Shelf‐life extension of Fragaria × ananassa Duch. using selenium nanoparticles synthesized from Cassia fistula Linn. leaves

“…Fruits and vegetables lose water through transpiration and respiration soon after harvest. And decrease in water content results in the reduction of weight and freshness (Singh et al, 2022 ).…”

Emerging trends in the agri-food sector: Digitalisation and shift to plant-based diets

“…Big Data Analytics (BDA) is the process of using sophisticated analytics on BD ( Ciccullo et al, 2022 ). The most current or potential applications of BD in vegetables, fruits and other plant-based sectors include optimal planting of fruit trees using data extracted from satellite and unmanned aerial vehicle imagery ( Saldana Ochoa and Guo, 2019 ), characterisation of size and shape phenotypes of horticultural crops using high throughput imagery ( Haque et al, 2021 ), improvement of controlled environment agriculture, such as soilless hydroponics and others for vegetable and fruit farming ( Ragaveena et al, 2021 ), and mitigating post-harvest losses and managing fruit and vegetable quality through machine learning ( Singh et al, 2022 ).…”

Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds

“…In recent times, the use of AI in the felds of health, manufacturing, agriculture, and academia domain has grown in many folds [8][9][10]. Te role of blockchain, IoT, and WSAN is well-known and popular among researchers due to their reasonably good advantages available at low cost [11][12][13]. Industry 4.0 and 5G/6G telecommunication have revolutionized the applications in the felds of health, manufacturing, agriculture, and academia.…”