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Agronomy
Special Issues
The Development and Application of Machine Learning in Agriculture
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The Development and Application of Machine Learning in Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 2583

Special Issue Editor

Dr. Fei Ma

E-Mail Website
Guest Editor
The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China
Interests: infrared spectroscopy; laser-induced breakdown spectroscopy; proximal sensing; soil sensors; modelling and prediction

Special Issue Information

Dear Colleagues,

The development of modern agriculture needs to have support from advanced digital innovation techniques. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. Among them, data analysis is fundamental to making optimized decisions. Machine learning is an emerging technology in agriculture for data analysis, which has improved crop production and enhanced instantaneous monitoring, processing and collection in agriculture. This Special Issue will present papers reporting on the development and application of various machine learning techniques in agriculture. We are collecting full papers and high-quality reviews covering the application of various types of machine learning algorithms in solving relevant tasks and problems in agriculture. Topics may include the handling of large amounts of data collected during the entire growing season as well as the necessary software. We encourage you to share your research on machine learning applications in agriculture and to submit your paper to this Special Issue. 

Dr. Fei Ma
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning algorithms
  • agricultural products
  • soil and plant nutrition
  • crop health
  • precision agriculture
  • proximal/remote sensing

Published Papers (1 paper)

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Research

18 pages, 16496 KiB  
Article
Spatially Explicit Soil Acidification under Optimized Fertilizer Use in Sub-Saharan Africa
by Yves Uwiragiye, Mbezele Junior Yannick Ngaba, Mingxia Yang, Ahmed S. Elrys, Zhujun Chen and Jianbin Zhou
Agronomy 2023, 13(3), 632; https://doi.org/10.3390/agronomy13030632 - 22 Feb 2023
Cited by 3 | Viewed by 2118
Abstract
Acidic soils (pH < 5.5) cover roughly 30% of Sub-Saharan Africa. Low nitrogen fertilizer application (15 kg N ha−1 yr−1) has no effect on soil acidification in Sub-Saharan Africa (SSA). However, the effect of optimized fertilizer use on soil acidification [...] Read more.
Acidic soils (pH < 5.5) cover roughly 30% of Sub-Saharan Africa. Low nitrogen fertilizer application (15 kg N ha−1 yr−1) has no effect on soil acidification in Sub-Saharan Africa (SSA). However, the effect of optimized fertilizer use on soil acidification (H+) in SSA crops remains unknown. This study intended to predict the spatial variation of H+ caused by optimized fertilizer use using data from 5782 field trials in SSA cropland. We used ensemble machine learning to predict spatial variation (H+) after measuring the inputs and outputs of major elements and their effect on H+ production. The results revealed that H+ ranged spatially from 0 to 16 keq H+ ha−1 yr−1. The most protons (H+) were produced by cassava, banana, and Irish potatoes systems with 12.0, 9.8, and 8.9 keq H+ ha−1 yr−1, respectively. The results of the 10-fold cross validation for the soil acidification model were a coefficient of determination (R2) of 0.6, a root mean square error (RMSE) of 2.1, and a mean absolute error (MAE) of 1.4. Net basic cation loss drives soil acidification under optimized fertilizer application and climate covariates had a higher relative importance than other covariates. Digital soil mapping can produce soil acidification maps for sustainable land use and management plans. Full article
(This article belongs to the Special Issue The Development and Application of Machine Learning in Agriculture)
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