Justifying the prediction of major soil nutrients levels (N, P, and K) in cabbage cultivation.

Thilina Abekoon, Hirushan Sajindra, B L S K Buthpitiya, Namal Rathnayake, D P P Meddage, Upaka Rathnayake

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Article in MethodsX, 2024. 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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Beyond wind speed: Integrating oceanic indices and time-lagged features for superior wind energy prediction.PloS one · 2026
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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

6 authors.

Thilina AbekoonWater Resources Management and Soft Computing Research Laboratory, Millennium City, Athurugiriya, 10150, Sri Lanka.

Hirushan SajindraWater Resources Management and Soft Computing Research Laboratory, Millennium City, Athurugiriya, 10150, Sri Lanka.

B L S K ButhpitiyaDepartment of Chemistry, Faculty of Science, University of Kelaniya, 11600, Sri Lanka.

Namal RathnayakeGraduate School of Engineering, The University of Tokyo, Tokyo, 113-8654, Japan.

D P P MeddageSchool of Engineering and Information Technology, University of New South Wales, Northcott Drive, Canberra, ACT 2612, Australia.

Upaka RathnayakeDepartment of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, Sligo, F91 YW50, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In a recent paper by Sajindra et al. [1], the soil nutrient levels, specifically nitrogen, phosphorus, and potassium, in organic cabbage cultivation were predicted using a deep learning model. This model was designed with a total of four hidden layers, excluding the input and output layers, with each hidden layer meticulously crafted to contain ten nodes. The selection of the tangent sigmoid transfer function as the optimal activation function for the dataset was based on considerations such as the coefficient of correlation, mean squared error, and the accuracy of the predicted results. Throughout this study, the objective is to justify the tangent sigmoid transfer function and provide mathematical justification for the obtained results.•This paper presents the comprehensive methodology for the development of deep neural network for predict the soil nutrient levels.•Tangent Sigmoid transfer function usage is justified in predictions.•Methodology can be adapted to any similar real-world scenarios.

Indexed as

Cabbage cultivationDeep neural networkDeep Neural Network (DNN)Soil nutrient levelsTanSig function

Identifiers

PMID38957375

DOI10.1016/j.mex.2024.102793

PMCPMC11217682

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LicenceCC BY

Read underepoch 390

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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.