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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: closed (25 December 2024) | Viewed by 10164

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; agritourism development
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 semimonthly 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 policies can be found here.

Published Papers (6 papers)

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Editorial

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3 pages, 147 KiB  
Editorial
Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management
by Magdalena Piekutowska, Gniewko Niedbała, Sebastian Kujawa and Tomasz Wojciechowski
Agriculture 2025, 15(5), 494; https://doi.org/10.3390/agriculture15050494 - 25 Feb 2025
Viewed by 343
Abstract
Combining machine learning algorithms with Earth observations has great potential in the context of crop monitoring and management, which is essential in the face of global challenges related to food security and climate change [...] Full article
(This article belongs to the Special Issue Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management)

Research

Jump to: Editorial, Other

17 pages, 19075 KiB  
Article
A Channel Attention-Driven Optimized CNN for Efficient Early Detection of Plant Diseases in Resource Constrained Environment
by Sana Parez, Naqqash Dilshad and Jong Weon Lee
Agriculture 2025, 15(2), 127; https://doi.org/10.3390/agriculture15020127 - 8 Jan 2025
Cited by 1 | Viewed by 966
Abstract
Agriculture is a cornerstone of economic prosperity, but plant diseases can severely impact crop yield and quality. Identifying these diseases accurately is often difficult due to limited expert availability and ambiguous information. Early detection and automated diagnosis systems are crucial to mitigate these [...] Read more.
Agriculture is a cornerstone of economic prosperity, but plant diseases can severely impact crop yield and quality. Identifying these diseases accurately is often difficult due to limited expert availability and ambiguous information. Early detection and automated diagnosis systems are crucial to mitigate these challenges. To address this, we propose a lightweight convolutional neural network (CNN) designed for resource-constrained devices termed as LeafNet. LeafNet draws inspiration from the block-wise VGG19 architecture but incorporates several optimizations, including a reduced number of parameters, smaller input size, and faster inference time while maintaining competitive accuracy. The proposed LeafNet leverages small, uniform convolutional filters to capture fine-grained details of plant disease features, with an increasing number of channels to enhance feature extraction. Additionally, it integrates channel attention mechanisms to prioritize disease-related features effectively. We evaluated the proposed method on four datasets: the benchmark plant village (PV), the data repository of leaf images (DRLIs), the newly curated plant composite (PC) dataset, and the BARI Sunflower (BARI-Sun) dataset, which includes diverse and challenging real-world images. The results show that the proposed performs comparably to state-of-the-art methods in terms of accuracy, false positive rate (FPR), model size, and runtime, highlighting its potential for real-world applications. 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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16 pages, 2672 KiB  
Article
Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles
by Gabriel Italo Novaes da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Wagner Martins dos Santos, Alan Cézar Bezerra, Elisiane Alba, Marcos Vinícius da Silva, Jhon Lennon Bezerra da Silva, Luciana Sandra Bastos de Souza, Gabriel Thales Barboza Marinho, Abelardo Antônio de Assunção Montenegro and Thieres George Freire da Silva
Agriculture 2024, 14(12), 2166; https://doi.org/10.3390/agriculture14122166 - 28 Nov 2024
Cited by 2 | Viewed by 882
Abstract
The objective of this study was to correlate the biophysical parameters of forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them with machine learning in different agricultural systems. Four experimental units were conducted. Units I and II [...] Read more.
The objective of this study was to correlate the biophysical parameters of forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them with machine learning in different agricultural systems. Four experimental units were conducted. Units I and II had different plant spacings (0.10, 0.20, 0.30, 0.40, and 0.50 m) with East–West and North–South planting directions, respectively. Unit III had row spacings (1.00, 1.25, 1.50, and 1.75 m), and IV had cutting frequencies (6, 9, 12 + 6, and 18 months) with the clones “Orelha de Elefante Mexicana”, “Miúda”, and “IPA Sertânia”. Plant height and width, cladode area index, fresh and dry matter yield (FM and DM), dry matter content, and fifteen vegetation indices of the visible range were analyzed. The RGBVI and ExGR indices stood out for presenting greater correlations with FM and DM. The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha−1 in experimental units I and II, III, and IV, respectively. The results showed potential in the application of machine learning with RGB images for predictive analysis of the biophysical parameters of forage cactus. 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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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
Cited by 1 | Viewed by 869
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 6 | Viewed by 2181
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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Other

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30 pages, 1745 KiB  
Systematic Review
Trends in Machine and Deep Learning Techniques for Plant Disease Identification: A Systematic Review
by Diana-Carmen Rodríguez-Lira, Diana-Margarita Córdova-Esparza, José M. Álvarez-Alvarado, Juan Terven, Julio-Alejandro Romero-González and Juvenal Rodríguez-Reséndiz
Agriculture 2024, 14(12), 2188; https://doi.org/10.3390/agriculture14122188 - 30 Nov 2024
Cited by 4 | Viewed by 4117
Abstract
This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks [...] Read more.
This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks (CNNs), which have shown promise in accurately identifying and classifying plant diseases from images. By analyzing studies published from 2019 to 2024, this work summarizes the common methodologies involving stages of data acquisition, preprocessing, segmentation, feature extraction, and prediction to develop robust ML models. The findings indicate that the incorporation of advanced image processing and ML algorithms significantly enhances disease detection capabilities, leading to the early and precise diagnosis of crop ailments. This can not only improve crop yield and quality but also reduce the dependency on chemical pesticides, contributing to more sustainable agricultural practices. Future research should focus on enhancing the robustness of these models to varying environmental conditions and expanding the datasets to include a wider variety of crops and diseases. CNN-based models, particularly specialized architectures like ResNet, are the most widely used in the studies reviewed, making up 42.36% of all models, with ResNet alone contributing 7.65%. This highlights ResNet’s appeal for tasks that demand deep architectures and sophisticated feature extraction. Additionally, SVM models account for 9.41% of the models examined. The prominence of both ResNet and MobileNet reflects a trend toward architectures with residual connections for deeper networks, alongside efficiency-focused designs like MobileNet, which are well-suited for mobile and edge applications. 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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