Evidence map›Paper›PMID 36366269›Full record

ArticleSensors (Basel, Switzerland)2022

Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield.

Syeda Iqra Hassan, Muhammad Mansoor Alam, Muhammad Yousuf Irfan Zia, Muhammad Rashid, Usman Illahi, Mazliham Mohd Su'ud

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Syeda Iqra HassanDepartment of Electronics and Electrical Engineering, Universiti Kuala Lumpur British Malaysian Institute (UniKL BMI), Batu 8, Jalan Sungai Pusu, Gombak 53100, Malaysia.ORCID 0000-0001-6852-8993
Muhammad Mansoor AlamFaculty of Computing, Riphah International University, Islamabad 46000, Pakistan.ORCID 0000-0001-5773-7140
Muhammad Yousuf Irfan ZiaDepartment of Electrical Engineering, Ziauddin University, Karachi 74600, Pakistan.ORCID 0000-0003-4181-5997
Muhammad RashidDepartment of Computer Engineering, Umm Al Qura University, Makkah 21955, Saudi Arabia.ORCID 0000-0001-5852-1296
Usman IllahiDepartment of Electrical Engineering, FET, Gomal University, Dera Ismail Khan 29050, Pakistan.ORCID 0000-0002-9394-5219
Mazliham Mohd Su'udFaculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode.

Indexed as

OryzaSoilCrops, AgriculturalGeographic Information SystemsPlant WeedsSoilaerial imaginaryGISrice cropsoil testingUAVyield estimation

Identifiers

PMID36366269
PMCPMC9659203

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Registered trials

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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.