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Agronomy
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Geospatial Artificial Intelligence (GeoAI) Applications in Agriculture for Smart Farming...
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Geospatial Artificial Intelligence (GeoAI) Applications in Agriculture for Smart Farming Solutions

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

Deadline for manuscript submissions: closed (25 July 2024) | Viewed by 2031

Special Issue Editors

Dr. Nguyenthanh Son

E-Mail Website
Guest Editor
Center for Space and Remote Sensing Research (CSRSR), National Central University, Taoyuan 32001, Taiwan
Interests: precision agriculture; remote sensing; climate change; crop management
Special Issues, Collections and Topics in MDPI journals
Dr. Chien-Hui Syu

E-Mail Website
Guest Editor
Division of Agricultural Chemistry, Taiwan Agricultural Research Institute (TARI), No.189, Zhongzheng Rd., Wufeng District, Taichung City 41362, Taiwan
Interests: soil chemistry; soil survey; rhizosphere chemistry; instrumental analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Cheng-Ru Chen

E-Mail Website
Guest Editor
Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli City 32001, Taoyuan County, Taiwan
Interests: environmental education; remote sensing for earth environment (RS); geographic information systems (GIS); spatial data analysis; statistics on spatial data; time-series data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Background

Studies using artificial intelligence and spatial datasets for crop monitoring have become important and gained interest among scientists worldwide. The rich historical archives and continuing acquisition of earth observation datasets provide opportunities for crop monitoring at local, regional, and global scales in response to the impacts of climate change. In addition, recent advances and applications of artificial intelligence algorithms make it possible to process many spatiotemporal datasets for crop growth and damage assessment, crop health analysis, crop yield and water requirements, and crop yield forecasting, which is extremely important for agronomists to devise successful strategies for a country to address food security issues.

Aim and scope

This Special Issue aims to collect research manuscripts related to the applications of artificial intelligence and earth observation datasets for such crop monitoring purposes at different scales across the globe. The topics include, but are not limited to, the following aspects: 

  • Geospatial analysis for precision irrigation in smart farming: Explore using GeoAI in optimizing irrigation strategies by integrating geospatial data, weather patterns, and machine learning algorithms to enhance water efficiency and crop yield.
  • Spatial data fusion approaches for comprehensive agricultural insights: Explore methodologies for integrating various datasets (e.g., geospatial, weather, soil, and crop information) using advanced data fusion techniques for informed decision making.
  • Integrating geospatial data and AI for smart crop monitoring: Investigate how remotely sensed data, coupled with AI, can revolutionize crop monitoring techniques for crop health, growth, and potential yield prediction.
  • Pest and disease detection in precision agriculture: Exploring the application of GeoAI technologies (e.g., computer vision and machine learning) to detect and diagnose crop diseases and pest infestations for early intervention and sustainable pest management in smart farming.

Data-driven climate risk assessment for precision agriculture: Develop data-driven approaches to assess climate risks in precision agriculture and how predictive modeling and machine learning can help farmers anticipate and mitigate the impact of climate-related challenges on crop yields and overall farm productivity.

Dr. Nguyenthanh Son
Dr. Chien-Hui Syu
Dr. Cheng-Ru Chen
Guest Editors

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

  • geospatial artificial intelligence
  • crop monitoring
  • agricultural systems
  • yield forecasting
  • crop type mapping
  • crop yield and water requirements
  • crop health and damage assessment
  • climate risk assessment

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Published Papers (1 paper)

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Research

26 pages, 41998 KiB  
Article
Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India
by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath and Jagadeeswaran Ramasamy
Agronomy 2024, 14(11), 2707; https://doi.org/10.3390/agronomy14112707 - 17 Nov 2024
Viewed by 1079
Abstract
Large-scale mapping of soil resources can be crucial and indispensable for several of the managerial applications and policy implications. With machine learning models being the most utilized modeling technique for digital soil mapping (DSM), the implementation of model-based deep learning methods for spatial [...] Read more.
Large-scale mapping of soil resources can be crucial and indispensable for several of the managerial applications and policy implications. With machine learning models being the most utilized modeling technique for digital soil mapping (DSM), the implementation of model-based deep learning methods for spatial soil predictions is still under scrutiny. In this study, soil continuous (pH and OC) and categorical variables (order and suborder) were predicted using deep learning–multi layer perceptron (DL-MLP) and one-dimensional convolutional neural networks (1D-CNN) for the entire state of Tamil Nadu, India. For training the deep learning models, 27,098 profile observations (0–30 cm) were extracted from the generated soil database, considering soil series as the distinctive stratum. A total of 43 SCORPAN-based environmental covariates were considered, of which 37 covariates were retained after the recursive feature elimination (RFE) process. The validation and test results obtained for each of the soil attributes for both the algorithms were most comparable with the DL-MLP algorithm depicting the attributes’ most intricate spatial organization details, compared to the 1D-CNN model. Irrespective of the algorithms and datasets, the R2 and RMSE values of the pH attribute ranged from 0.15 to 0.30 and 0.97 to 1.15, respectively. Similarly, the R2 and RMSE of the OC attribute ranged from 0.20 to 0.39 and 0.38 to 0.42, respectively. Further, the overall accuracy (OA) of the order and suborder classification ranged from 39% to 67% and 35% to 64%, respectively. The explicit quantification of the covariate importance derived from the permutation feature importance implied that both the models tried to incorporate the covariate importance with respect to the genesis of the soil attribute under study. Such approaches of the deep learning models integrating soil–environmental relationships under limited parameterization and computing costs can serve as a baseline study, emphasizing opportunities in increasing the transferability and generalizability of the model while accounting for the associated environmental dependencies. Full article
(This article belongs to the Special Issue Geospatial Artificial Intelligence (GeoAI) Applications in Agriculture for Smart Farming Solutions)
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