ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Technical Advances in Plant Science

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1520163

This article is part of the Research TopicUAVs for Crop Protection: Remote Sensing, Prescription Mapping and Precision SprayingView all 10 articles

Decision Support System for Lespedeza Cuneata Production and Quality Evaluation: A WebGIS Dashboard Approach to Precision Agriculture

Provisionally accepted
Sudhanshu S PandaSudhanshu S Panda1Aftab SiddiqueAftab Siddique2*Thomas TerrillThomas Terrill2Ajit K. MahapatraAjit K. Mahapatra2Eric R MorganEric R Morgan3Andres A Pech-CervantesAndres A Pech-Cervantes4Jan A Van WykJan A Van Wyk5
  • 1University of North Georgia, Oakwood, Georgia, United States
  • 2Fort Valley State University, Fort Valley, United States
  • 3Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
  • 4Prairie View A&M University, Prairie View, Texas, United States
  • 5University of Pretoria, Pretoria, South Africa

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Small-scale farmers in the southeastern United States face increasing challenges in sustaining forage production due to erratic rainfall, poor soils, and limited access to precision agricultural tools. These constraints demand site-specific solutions that integrate climate resilience with sustainable land use. This study introduces a pioneering Site-Specific Fodder Management Decision Support System (SSFM-DSS) designed to optimize the cultivation of Lespedeza cuneata (sericea lespedeza), a drought-tolerant, nitrogen-fixing legume well-suited for marginal lands. By integrating high-resolution geospatial technologies-Geographic Information Systems (GIS), Global Navigation Satellite Systems (GNSS), and remote sensing-with empirical field data and predictive modeling, we have developed an automated suitability framework for SL cultivation across Alabama, Georgia, and South Carolina. The model incorporates multi-criteria environmental parameters, including soil characteristics, topography, and climate variability, to generate spatially explicit recommendations. To translate these insights into actionable strategies, we also developed a farmer-focused WebGIS Dashboard that delivers real-time, location-based guidance for SL production. Our findings underscore the significant potential of SSFM-DSS to enhance fodder availability, improve system resilience under climate stress, and promote sustainable livestock production. This integrative approach offers a promising pathway for climate-smart agriculture, supporting broader food security objectives in vulnerable agroecosystems.

Keywords: Site-specific Fodder Management, Sericea lespedeza, Geographic Information Systems, remote sensing, precision agric ulture

Received: 30 Oct 2024; Accepted: 24 Jun 2025.

Copyright: © 2025 Panda, Siddique, Terrill, Mahapatra, Morgan, Pech-Cervantes and Van Wyk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Aftab Siddique, Fort Valley State University, Fort Valley, United States

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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