Optimization Techniques for Crop Planning: Current Achievements and Future Directions

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (25 November 2024) | Viewed by 1841

Special Issue Editors


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Guest Editor
Bond Business School, Bond University, Queensland, Australia
Interests: digital agriculture; crop planning; optimisation climate change; computational science
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Guest Editor
School of Information and Communication Technology, University of Tasmania, Hobart, Australia
Interests: evolutionary computation; computational agriculture; multiobjective optimisation; ecoacoustics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia
Interests: computational science; multiobjective optimisation; smart agriculture; scientific visualisation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will explore the latest advancements and future trajectories in optimization techniques for crop planning. Crop planning plays a crucial role in agricultural productivity, resource management, and sustainability. As such, the integration of optimization methodologies holds immense potential for enhancing decision-making processes in agriculture.

We invite submissions focusing on various aspects of optimization techniques applied to crop planning, including, but not limited to, the following:

  • Mathematical modeling approaches for crop planning optimization;
  • Integration of machine learning and artificial intelligence techniques into crop planning optimization;
  • Multi-objective optimization for sustainable agricultural practices;
  • Optimization of resource allocation and use in crop production;
  • Decision support systems for precision agriculture using optimization techniques;
  • Case studies and applications of optimization methods in real-world crop planning scenarios;
  • Novel algorithms and computational methods for crop planning optimization.

We encourage contributions from researchers, practitioners, and stakeholders working in the fields of agriculture, optimization, computational intelligence, and related disciplines. Submissions should present original research findings, methodologies, or critical reviews that contribute to the advancement of optimization techniques for crop planning.

Prof. Dr. Marcus Randall
Dr. James Montgomery
Dr. Andrew Lewis
Guest Editors

Manuscript Submission Information

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Keywords

  • crop planning
  • multi-objective optimization
  • machine learning/AI
  • agricultural modeling
  • climate change

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Published Papers (1 paper)

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Research

18 pages, 4238 KiB  
Article
Combining Vegetation Indices to Identify the Maize Phenological Information Based on the Shape Model
by Huizhu Wu, Bing Liu, Bingxue Zhu, Zhijun Zhen, Kaishan Song and Jingquan Ren
Agriculture 2024, 14(9), 1608; https://doi.org/10.3390/agriculture14091608 - 14 Sep 2024
Cited by 1 | Viewed by 1389
Abstract
Maize is the world’s largest food crop and plays a critical role in global food security. Accurate phenology information is essential for improving yield estimation and enabling timely field management. Yet, much of the research has concentrated on general crop growth periods rather [...] Read more.
Maize is the world’s largest food crop and plays a critical role in global food security. Accurate phenology information is essential for improving yield estimation and enabling timely field management. Yet, much of the research has concentrated on general crop growth periods rather than on pinpointing key phenological stages. This gap in understanding presents a challenge in determining how different vegetation indices (VIs) might accurately extract phenological information across these stages. To address this, we employed the shape model fitting (SMF) method to assess whether a multi-index approach could enhance the precision of identifying key phenological stages. By analyzing time-series data from various VIs, we identified five phenological stages (emergence, seven-leaf, jointing, flowering, and maturity stages) in maize cultivated in Jilin Province. The findings revealed that each VI had distinct advantages depending on the phenological stage, with the land surface water index (LSWI) being particularly effective for jointing and flowering stages due to its correlation with vegetation water content, achieving a root mean square error (RMSE) of three to four days. In contrast, the normalized difference vegetation index (NDVI) was more effective for identifying the emergence and seven-leaf stages, with an RMSE of four days. Overall, combining multiple VIs significantly improved the accuracy of phenological stage identification. This approach offers a novel perspective for utilizing diverse VIs in crop phenology, thereby enhancing the precision of agricultural monitoring and management practices. Full article
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