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Agriculture
Special Issues
Computational, AI and IT Solutions Helping Agriculture
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Computational, AI and IT Solutions Helping Agriculture

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

Deadline for manuscript submissions: 25 September 2024 | Viewed by 3931

Special Issue Editor

Dr. Dimitre Dimitrov

E-Mail Website
Guest Editor
Department of University Transfer, Faculty of Arts & Sciences, NorQuest College, Edmonton, AB T5J 1L6, Canada
Interests: mathematical-process-based and machine learning modeling; ecohydrology; biogeochemistry; ecosystem productivity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a natural continuation of our previous Special Issue, titled “Internet and Computers for Agriculture”; this one extends further, with the aim of covering recent and current progress in the application of computational solutions, artificial intelligence (AI), and information technologies (IT) in modern agriculture. Nowadays, rapid changes are taking place at a planetary scale, including human population growth and global climatic and ecological changes, resulting in a call for immediate sustainable and secure smart solutions for food production, water supply, greenhouse (GHG) gas emissions, and environmental health.

This Special Issue provides a stage for the innovative research of scientists and entrepreneurs involved in the development and application of various software products, and digital solutions for agriculture, agroecosystems, and natural ecosystems with application in agriculture, to be presented. We welcome the submission of original articles and reviews involving mobile apps, web applications, internet platforms, Internet of Things (IoT) devices, cloud technologies, AI and machine learning (ML) methods and applications for precision agriculture, monitoring, cultivation, harvesting, marketing, management, decision making, weather forecasting, optimization, natural language processing, computer/machine vision, drones, real time detection systems, sensors for field operations, smart agriculture machinery, diagnostics, species and disease recognition, big data collection, scientific-process-based mathematical modeling, and machine learning modeling, which can contribute to modern agriculture now and in the future.

Dr. Dimitre Dimitrov
Guest Editor

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

  • Smart agriculture
  • Web applications
  • Web platforms
  • Mobile apps
  • IoT devices
  • Cloud computing
  • AI and Machine learning
  • Big data
  • Data driven modeling
  • Process-based modeling

Published Papers (2 papers)

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Research

24 pages, 9320 KiB  
Article
Precision Corn Pest Detection: Two-Step Transfer Learning for Beetles (Coleoptera) with MobileNet-SSD
by Edmond Maican, Adrian Iosif and Sanda Maican
Agriculture 2023, 13(12), 2287; https://doi.org/10.3390/agriculture13122287 - 18 Dec 2023
Viewed by 1543
Abstract
Using neural networks on low-power mobile systems can aid in controlling pests while preserving beneficial species for crops. However, low-power devices require simplified neural networks, which may lead to reduced performance. This study was focused on developing an optimized deep-learning model for mobile [...] Read more.
Using neural networks on low-power mobile systems can aid in controlling pests while preserving beneficial species for crops. However, low-power devices require simplified neural networks, which may lead to reduced performance. This study was focused on developing an optimized deep-learning model for mobile devices for detecting corn pests. We propose a two-step transfer learning approach to enhance the accuracy of two versions of the MobileNet SSD network. Five beetle species (Coleoptera), including four harmful to corn crops (belonging to genera Anoxia, Diabrotica, Opatrum and Zabrus), and one beneficial (Coccinella sp.), were selected for preliminary testing. We employed two datasets. One for the first transfer learning procedure comprises 2605 images with general dataset classes ‘Beetle’ and ‘Ladybug’. It was used to recalibrate the networks’ trainable parameters for these two broader classes. Furthermore, the models were retrained on a second dataset of 2648 images of the five selected species. Performance was compared with a baseline model in terms of average accuracy per class and mean average precision (mAP). MobileNet-SSD-v2-Lite achieved an mAP of 0.8923, ranking second but close to the highest mAP (0.908) obtained by MobileNet-SSD-v1 and outperforming the baseline mAP by 6.06%. It demonstrated the highest accuracy for Opatrum (0.9514) and Diabrotica (0.8066). Anoxia it reached a third-place accuracy (0.9851), close to the top value of 0.9912. Zabrus achieved the second position (0.9053), while Coccinella was reliably distinguished from all other species, with an accuracy of 0.8939 and zero false positives; moreover, no pest species were mistakenly identified as Coccinella. Analyzing the errors in the MobileNet-SSD-v2-Lite model revealed good overall accuracy despite the reduced size of the training set, with one misclassification, 33 non-identifications, 7 double identifications and 1 false positive across the 266 images from the test set, yielding an overall relative error rate of 0.1579. The preliminary findings validated the two-step transfer learning procedure and placed the MobileNet-SSD-v2-Lite in the first place, showing high potential for using neural networks on real-time pest control while protecting beneficial species. Full article
(This article belongs to the Special Issue Computational, AI and IT Solutions Helping Agriculture)
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22 pages, 18514 KiB  
Article
Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance
by Jie Chen, Xiaochun Hu, Jiahao Lu, Yan Chen and Xin Huang
Agriculture 2023, 13(11), 2110; https://doi.org/10.3390/agriculture13112110 - 07 Nov 2023
Viewed by 1104
Abstract
The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, [...] Read more.
The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, an efficient real-time model designed to detect, track, and count wheat ears in video sequences. This model uses FasterNet as its foundational feature extraction network, significantly reducing the model’s parameter count and improving the model’s inference speed. We also incorporate deformable convolutions and dynamic sparse attention into the feature extraction network to enhance its ability to capture wheat ear features while reducing the effects of intricate environmental conditions. To address information loss during up-sampling and strengthen the model’s capacity to extract wheat ear features across varying feature map scales, we integrate a path aggregation network (PAN) with the content-aware reassembly of features (CARAFE) up-sampling operator. Furthermore, the incorporation of the Kalman filter-based target-tracking algorithm, Observation-centric SORT (OC-SORT), enables real-time tracking and counting of wheat ears within expansive field settings. Experimental results demonstrate that Wheat-FasterYOLO achieves a mean average precision (mAP) score of 94.01% with a small memory usage of 2.87MB, surpassing popular detectors such as YOLOX and YOLOv7-Tiny. With the integration of OC-SORT, the composite higher order tracking accuracy (HOTA) and counting accuracy reached 60.52% and 91.88%, respectively, while maintaining a frame rate of 92 frames per second (FPS). This technology has promising applications in wheat ear counting tasks. Full article
(This article belongs to the Special Issue Computational, AI and IT Solutions Helping Agriculture)
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