Evidence map›Paper›PMID 42910018›Full record

ReviewCurrent research in food science2026

Artificial intelligence and biotechnology for smart storage: Quality and safety perspectives in horticultural produce.

Yuan Sui, Yu Zhao, Wenxuan Chen, Bin Duan, Zhenshuo Wang, Zhuo Chen

Abstract readReview
In one paragraph

Review in Current research in food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yuan SuiState Key Laboratory of Green Pesticides, Guizhou University, Guiyang, 550025, China.
Yu ZhaoState Key Laboratory of Agricultural and Forestry Biosecurity, MARA Key Lab of Surveillance and Management for Plant Quarantine Pests, College of Plant Protection, China Agricultural University, Beijing, 100193, China.
Wenxuan ChenState Key Laboratory of Agricultural and Forestry Biosecurity, MARA Key Lab of Surveillance and Management for Plant Quarantine Pests, College of Plant Protection, China Agricultural University, Beijing, 100193, China.
Bin DuanChongqing Key Laboratory for Germplasm Innovation of Special Aromatic Spice Plants, College of Smart Agriculture/Institute of Special Plants, Chongqing University of Arts and Sciences, Yongchuan, Chongqing, 402160, China.
Zhenshuo WangState Key Laboratory of Agricultural and Forestry Biosecurity, MARA Key Lab of Surveillance and Management for Plant Quarantine Pests, College of Plant Protection, China Agricultural University, Beijing, 100193, China.
Zhuo ChenState Key Laboratory of Green Pesticides, Guizhou University, Guiyang, 550025, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Addressing the global challenge of postharvest losses in horticultural produce requires the continuous development of innovative strategies that integrate cutting-edge technology with fundamental biological knowledge. This review explores the synergy between artificial intelligence (AI), biotechnology, and traditional postharvest management strategies to improve the quality, safety, and shelf-life of fruits and vegetables (F&V). We examine the physiological and molecular mechanisms driving postharvest deterioration, including respiration, senescence, and host-pathogen interactions, while evaluating how AI-driven tools are advancing quality monitoring, predictive analytics, and supply chain optimization. Furthermore, we assess the potential of advanced biotechnological interventions, such as CRISPR/Cas9 gene editing and RNA interference techniques, to extend shelf-life and enhance the disease resistance in horticultural crops. By evaluating recent progress in these domains, we present integrated strategies that combine biological insight with digital transformation for sustainable postharvest management. The effective implementation of these approaches holds considerable potential to reduce F&V waste, enhance food security, and create horticultural supply chains that are more resilient to ever growing global production and evolving environmental challenges.

Indexed as

Artificial intelligenceBiotechnologyHorticultural productsMachine learningPostharvest quality

Identifiers

PMID42910018
PMCPMC13685987

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.