Evidence map›Paper›PMID 38905232›Full record

ArticlePloS one2024

Designing and development of agricultural rovers for vegetable harvesting and soil analysis.

Bristy Das, Tahmid Zarif Ul Hoq Sayor, Rubyat Jahan Nijhum, Mehnaz Tabassum Tishun, Taiyeb Hasan Sakib, Md Ehsanul Karim, Afm Jamal Uddin, Aparna Islam, Abu S M Mohsin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2024. 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

9 authors.

Bristy DasEEE Department, Nanotechnology, IoT and Applied Machine Learning Research Group, Brac University, Dhaka, Bangladesh.ORCID 0000-0002-9708-5359
Tahmid Zarif Ul Hoq SayorEEE Department, Nanotechnology, IoT and Applied Machine Learning Research Group, Brac University, Dhaka, Bangladesh.ORCID 0009-0009-4125-588X
Rubyat Jahan NijhumEEE Department, Nanotechnology, IoT and Applied Machine Learning Research Group, Brac University, Dhaka, Bangladesh.ORCID 0009-0003-8786-5962
Mehnaz Tabassum TishunEEE Department, Nanotechnology, IoT and Applied Machine Learning Research Group, Brac University, Dhaka, Bangladesh.ORCID 0009-0009-4981-3276
Taiyeb Hasan SakibEEE Department, Nanotechnology, IoT and Applied Machine Learning Research Group, Brac University, Dhaka, Bangladesh.
Md Ehsanul KarimEEE Department, Nanotechnology, IoT and Applied Machine Learning Research Group, Brac University, Dhaka, Bangladesh.ORCID 0000-0002-3948-7368
Afm Jamal UddinDepartment of Horticulture, Sher-e-Bangla Agricultural University (SAU), Dhaka, Bangladesh.
Aparna IslamDepartment of Mathematics and Natural Sciences, Biotechnology Program, Brac University, Dhaka, Bangladesh.
Abu S M MohsinEEE Department, Nanotechnology, IoT and Applied Machine Learning Research Group, Brac University, Dhaka, Bangladesh.ORCID 0000-0001-9281-9088

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover's hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN's SDG 2 Zero Hunger goals.

Indexed as

AgricultureSoilVegetablesAlgorithmsCrop ProductionCrops, AgriculturalDeep LearningRoboticsSolanum lycopersicumSoil

Identifiers

PMID38905232
PMCPMC11192377

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.