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Agriculture
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Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring...
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Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management

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

Deadline for manuscript submissions: 25 December 2024 | Viewed by 1823

Special Issue Editors

Prof. Dr. Gniewko Niedbała

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Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
Special Issues, Collections and Topics in MDPI journals
Dr. Magdalena Piekutowska

E-Mail Website
Guest Editor
Department of Geoecology and Geoinformation, Institute of Biology and Earth Sciences, Pomeranian University in Słupsk, 27 Partyzantów St., 76-200 Słupsk, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; potato production; plant breeding; soil science; plant growth analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Sebastian Kujawa

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
Special Issues, Collections and Topics in MDPI journals
Dr. Tomasz Wojciechowski

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: agricutural engineering; soil tillage; precison agriculture; soil monitoring, proximal sensing, spectroscopy; digital farming; smart farming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Agriculture delves into the transformative potential of combining machine learning algorithms with Earth observations for enhanced crop monitoring and management. These activities provide a better understanding of the mechanisms that regulate plant growth and development, starting with optimal conditions and ending with abnormal, difficult conditions that trigger numerous nonstandard defense responses. In the face of ongoing climate change and its impact on global food security, the integration of advanced technologies such as digital imaging, satellite data, UAV imagery, and machine learning has become indispensable.

Recent advancements in machine learning algorithms, coupled with extensive historical archives and the continuous acquisition of earth observation data, provide unparalleled opportunities to monitor crop growth, health, and yield at various scales. By integrating machine learning with spatial datasets, precise assessments of crop conditions can be achieved, facilitating the development of innovative strategies to boost productivity and sustainability in agriculture.   

We invite contributions that explore the following themes:

Geospatial Analysis for Precision Irrigation: Utilizing GeoAI to optimize irrigation strategies by integrating geospatial data, weather patterns, and machine learning to enhance water efficiency and crop yield.

Spatial Data Fusion for Agricultural Insights: Methodologies for integrating diverse datasets (e.g., geospatial, weather, soil, and crop information) using advanced data fusion techniques for informed decision-making.

Smart Crop Monitoring: Investigating how remotely sensed data, coupled with ML, can revolutionize crop health, growth, and yield prediction.

Pest and Disease Detection: Applying GeoAI technologies such as computer vision and machine learning to detect and diagnose crop diseases and pest infestations for early intervention and sustainable pest management.

Data-Driven Climate Risk Assessment: Developing predictive models to assess climate risks in precision agriculture, helping farmers mitigate climate-related challenges.

This Special Issue aims to present high-level research that not only showcases case studies but also highlights the potential, limitations, and criticalities of integrating these technologies in agriculture. We particularly encourage submissions that demonstrate the economic and environmental impacts of these applications, contributing to the ongoing development of sustainable agricultural practices.

Prof. Dr. Gniewko Niedbała
Dr. Magdalena Piekutowska
Dr. Sebastian Kujawa
Dr. Tomasz Wojciechowski
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. Agriculture 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

  • precision agriculture
  • remote sensing
  • artificial intelligence
  • crop monitoring
  • UAV imagery
  • geospatial data
  • smart farming
  • data fusion
  • machine learning
  • satellite data
  • pest detection
  • climate risk assessment

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

13 pages, 3096 KiB  
Article
Defoliation Categorization in Soybean with Machine Learning Algorithms and UAV Multispectral Data
by Marcelo Araújo Junqueira Ferraz, Afrânio Gabriel da Silva Godinho Santiago, Adriano Teodoro Bruzi, Nelson Júnior Dias Vilela and Gabriel Araújo e Silva Ferraz
Agriculture 2024, 14(11), 2088; https://doi.org/10.3390/agriculture14112088 - 19 Nov 2024
Viewed by 289
Abstract
Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as random forest, for categorizing defoliation levels in R7-stage soybean plants. This research assesses the [...] Read more.
Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as random forest, for categorizing defoliation levels in R7-stage soybean plants. This research assesses the effectiveness of vegetation indices, spectral bands, and relative vegetation cover as input parameters, demonstrating that machine learning approaches combined with multispectral imagery can provide a more accurate and efficient assessment of Asian soybean rust in commercial soybean fields. The random forest algorithm exhibited satisfactory classification performance when compared to recent studies, achieving accuracy, precision, recall, F1-score, specificity, and AUC values of 0.94, 0.92, 0.92, 0.92, 0.97, and 0.97, respectively. The input variables identified as most important for the classification model were the WDRVI and MPRI indices, the red-edge and NIR bands, and relative vegetation cover, with the highest Gini importance index. Full article
(This article belongs to the Special Issue Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management)
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24 pages, 6908 KiB  
Article
LP-YOLO: A Lightweight Object Detection Network Regarding Insect Pests for Mobile Terminal Devices Based on Improved YOLOv8
by Yue Yu, Qi Zhou, Hao Wang, Ke Lv, Lijuan Zhang, Jian Li and Dongming Li
Agriculture 2024, 14(8), 1420; https://doi.org/10.3390/agriculture14081420 - 21 Aug 2024
Cited by 1 | Viewed by 1087
Abstract
To enhance agricultural productivity through the accurate detection of pests under the constrained resources of mobile devices, we introduce LP-YOLO, a bespoke lightweight object detection framework optimized for mobile-based insect pest identification. Initially, we devise lightweight components, namely LP_Unit and LP_DownSample, to serve [...] Read more.
To enhance agricultural productivity through the accurate detection of pests under the constrained resources of mobile devices, we introduce LP-YOLO, a bespoke lightweight object detection framework optimized for mobile-based insect pest identification. Initially, we devise lightweight components, namely LP_Unit and LP_DownSample, to serve as direct substitutes for the majority of modules within YOLOv8. Subsequently, we develop an innovative attention mechanism, denoted as ECSA (Efficient Channel and Spatial Attention), which is integrated into the network to forge LP-YOLO(l). Moreover, assessing the trade-offs between parameter reduction and computational efficiency, considering both the backbone and head components of the network, we use structured pruning methods for the pruning process, culminating in the creation of LP-YOLO(s). Through a comprehensive series of evaluations on the IP102 dataset, the efficacy of LP-YOLO as a lightweight object detection model is validated. By incorporating fine-tuning techniques during training, LP-YOLO(s)n demonstrates a marginal mAP decrease of only 0.8% compared to YOLOv8n. However, it achieves a significant reduction in parameter count by 70.2% and a remarkable 40.7% increase in FPS, underscoring its efficiency and performance. Full article
(This article belongs to the Special Issue Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management)
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