Evidence map›Paper›PMID 41326634›Full record

ArticleScientific reports2025

Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning.

Abdallah Elshawadfy Elwakeel, Abdallah Zein Elden, Saad F Ahmed, Sali Issa, Changyou Li, Khaled Abdeen Mousa Ali, Waleed M Hanafy, Guma Ali, Fawaz Alzahrani, Atef Fathy Ahmed

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In one paragraph

Article in Scientific reports, 2025. 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

10 authors.

Abdallah Elshawadfy ElwakeelAgricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, 81528, Egypt. abdallah_elshawadfy@agr.aswu.edu.eg.
Abdallah Zein EldenAgricultural and Biosystems Engineering Department, Faculty of Agriculture, Alexandria University, Alshatby, Alexandria, Egypt.
Saad F AhmedAgricultural and Biosystems Engineering Department, Faculty of Agriculture, Alexandria University, Alshatby, Alexandria, Egypt.
Sali IssaElectrical Information of Science and Technology, Hubei University of Education, Wuhan, China. dr.sali.issa@gmail.com.
Changyou LiCollege of Engineering, South China Agricultural University, Guangzhou, 510642, China.
Khaled Abdeen Mousa AliCollege of Engineering, South China Agricultural University, Guangzhou, 510642, China. khaledabdeen@azhar.edu.eg.
Waleed M HanafyAgricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, 81528, Egypt.
Guma AliDepartment of Computer and Information Science, Faculty of Technoscience, Muni University, Arua, Uganda. a.guma@muni.ac.ug.
Fawaz AlzahraniDepartment of Biology, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
Atef Fathy AhmedDepartment of Biology, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sugarcane is a vital global crop, serving as a primary source of sugar, biofuel, and renewable energy. Advancements in harvesting are critical to meeting rising demand, enhancing profitability, and supporting eco-friendly agricultural practices in the sugarcane sector. Based on the current challenges of sugarcane harvesting in developed countries, the current study aimed to develop a semiautomatic whole-stalk sugarcane harvester (SWSH) for harvesting two rows of sugarcane stalks at a time and to be front-mounted on a classic four-wheel agricultural tractor. Then performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using Feedforward Neural Network (FNN) and Deep Neural Network (DNN) at different levels of forward speeds (3, 3.5, 4.5, and 5 km/h), row spacing (71, 78.89, and 88.75 cm), cutting heights (0, 2, and 4 cm), and numbers of knives (2 and 4) of the cutting systems. The obtained results showed that the cutting efficiency of the developed SWSH reached 100%. Where the higher cutting efficiency was observed at a cutting height equal to zero (ground level), forward speed of 3 km/hand row spacing of 71 cm using both 2 and 4 knives. The minimum total operating cost of the developed SWSH was about 4.42 USD/ha, and it was detected when using a forward speed of 4.5 km/h, row spacing of 88.75 cm, a cutting height of 4 cm, and two knives only on the cutting disk. Furthermore, at a row spacing of 88.75 cm, the maximum field capacity of the developed SWSH was 0.554 ha/h, observed at a forward speed of 4.5 km/h.

Indexed as

Agricultural engineeringArtificial intelligence (AI)Farm machineryNeural networksSugarcane harvesting

Identifiers

PMID41326634
PMCPMC12672697

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LicenceCC BY-NC-ND
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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.