Evidence map›Paper›PMID 42428547›Full record

ArticlePeerJ2025

Pre-harvest mango yield prediction using artificial neural networks based on leaf nutrient variability.

Abdullah Alebidi, Khalid F Almutairi, Rashid S Al-Obeed, Essa Makhasha, Abdulwahed M Aboukarima, Mahmoud Abdel-Sattar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 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

6 authors.

Abdullah AlebidiDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Khalid F AlmutairiDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Rashid S Al-ObeedDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Essa MakhashaDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Abdulwahed M AboukarimaDepartment of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Mahmoud Abdel-SattarDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate pre-harvest mango yield prediction provides valuable insights for improving productivity, reducing food waste, enhancing food security, and supporting the farmer livelihoods. In this respect, nine mango orchards, which had different agricultural practices, were selected to attain the essential data to integrate yield and leaf nutrient variability using an artificial neural network model. In the mango leaves, noticeable variations were detected in concentrations of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), chlorophyll

Indexed as

MangiferaNeural Networks, ComputerNutrientsPlant LeavesAgricultureChlorophyllNitrogenPhosphorusPrediction AlgorithmsSeasonsChlorophyllNitrogenPhosphorusCarbohydrateChlorophyllC/N ratioNutrientPredictionProductivitySustainable

Identifiers

PMID42428547
PMCPMC13348870

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.