Evidence map›Paper›PMID 40686912›Full record

ReviewFood chemistry: X2025

Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce.

Mansi Nautiyal, Saloni Joshi, Iqbal Hussain, Hrithik Rawat, Akanksha Joshi, Aditi Saini, Rishiraj Kapoor, Himani Verma, Anshul Nautiyal, Aniket Chikara and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026
    Review
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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

12 authors.

Mansi NautiyalDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Saloni JoshiDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Iqbal HussainGeneral Studies Department, Jubail Industrial College, Jubail Industrial City, Jubail 31961, The Kingdom of Saudi Arabia.
Hrithik RawatDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Akanksha JoshiDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Aditi SainiDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Rishiraj KapoorDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Himani VermaDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Anshul NautiyalDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Aniket ChikaraDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Waseem AhmadDepartment of Chemistry, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.
Sanjay KumarDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating Artificial Intelligence (AI) in agriculture marks a new era of precision and efficiency. Convolutional Neural Networks (CNNs) enable early crop disease detection through image-based classification, reducing yield loss. Long Short-Term Memory (LSTM) networks support predictive modelling for yield forecasting and soil health assessment, aiding resource allocation. While mechanization and automation remain global challenges, modern AI and machine learning (ML) applications have transformed agricultural practices. This review explores various AI tools, including ML algorithms, deep learning (DL) models, Internet of Things (IoT), and Decision Support Systems (DSS), and their role in addressing challenges like maximizing crop yield, precision irrigation, pest control, and informed decision-making. The paper further highlights AI applications in plant breeding, irrigation, logistics, and packaging. Despite the advancements, widespread adoption faces barriers such as high costs, privacy concerns, inadequate infrastructure, and limited technical knowledge. The review offers insights into both the potential and limitations of AI in agriculture.

Indexed as

AgricultureArtificial intelligenceDeep learningInternet of thingsMachine learning

Identifiers

PMID40686912
PMCPMC12274707

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.