Evidence map›Paper›PMID 39371687›Full record

ArticleResearch (Washington, D.C.)2024

The Deep Learning-Crop Platform (DL-CRoP): For Species-Level Identification and Nutrient Status of Agricultural Crops.

Mohammad Urfan, Prakriti Rajput, Palak Mahajan, Shubham Sharma, Haroon Rashid Hakla, Verasis Kour, Bhubneshwari Khajuria, Rehana Chowdhary, Parveen Kumar Lehana, Namrata Karlupia and 3 more

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Article in Research (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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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

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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

2 citing papers in PubMed.

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4 · The record

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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

13 authors.

Mohammad UrfanCrop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India.
Prakriti RajputCrop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India.ORCID https://orcid.org/0009-0001-6672-965X
Palak MahajanDepartment of Computer Science & Engineering, Central University of Jammu, Jammu 181143, India.
Shubham SharmaCrop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India.
Haroon Rashid HaklaCrop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India.
Verasis KourDepartment of Electronics, University of Jammu, Jammu 180006, India.
Bhubneshwari KhajuriaCrop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India.ORCID https://orcid.org/0009-0006-8472-6512
Rehana ChowdharyCrop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India.
Parveen Kumar LehanaDepartment of Electronics, University of Jammu, Jammu 180006, India.
Namrata KarlupiaDepartment of Computer Science & IT, University of Jammu, Jammu 180006, India.
Pawanesh AbrolDepartment of Computer Science & IT, University of Jammu, Jammu 180006, India.
Lam Son Phan TranInstitute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA.ORCID https://orcid.org/0000-0001-9883-9768
Sikander Pal ChoudharyCrop Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India.ORCID https://orcid.org/0000-0002-3827-466X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield. The present study introduces a reliable deep learning platform called "Deep Learning-Crop Platform" (DL-CRoP) for the identification of some commercially grown plants and their nutrient requirements using leaf, stem, and root images using a convolutional neural network (CNN). It extracts intrinsic feature patterns through hierarchical mapping and provides remarkable outcomes in identification tasks. The DL-CRoP platform is trained on the plant image dataset, namely, Jammu University-Botany Image Database (JU-BID), available at https://github.com/urfanbutt. The findings demonstrate implementation of DL-CRoP-cases A (uses shoot images) and B (uses leaf images) for species identification for

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PMID39371687
PMCPMC11450475

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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.