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Agronomy
Special Issues
Applications of Deep Learning in Smart Agriculture—Volume II
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Special Issue "Applications of Deep Learning in Smart Agriculture—Volume II"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 August 2023 | Viewed by 1639

Special Issue Editors

Dr. Saeid Homayouni

E-Mail Website
Guest Editor
Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec City, QC G1K 9A9, Canada
Interests: remote sensing; geomatics; analysis of optical, SAR, and UAV Earth observations through artificial intelligence and machine learning approaches for agro-environmental applications
Special Issues, Collections and Topics in MDPI journals
Dr. Yacine Bouroubi

E-Mail Website
Guest Editor
Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Interests: remote sensing; deep learning; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Karem Chokmani

E-Mail Website
Guest Editor
Centre Eau Terre Environnement, INRS, 490 Rue de la Couronne, Québec, QC G1K 9A9, Canada
Interests: remote sensing; precision agriculture; deep learning; geomatics; spatial and temporal variability of water resources; microclimate; UAVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart agriculture, comprising precision agriculture, digital agriculture, and other new concepts in agricultural research and practice, has gained increasing attention in recent years due to the rising importance of sustainable food production and resource management, as well as to the opportunity offered by the emergence of several digital hardware and software technologies. Accordingly, the development of geospatial, information technology, Internet of Things, robotics, artificial intelligence, and data analytics applications plays an essential role in modern farm management. Traditional approaches of information and knowledge collection for the monitoring of agricultural fields is laborious, time-consuming, and may contain uncertainties. Therefore, technological advances in remote sensing platforms and sensors, digital web applications, and cloud data storage and management centers, as well as the development of intelligent data analysis methods and decision support systems, have improved the quality of monitoring of agricultural lands in order to meet agricultural requirements. Smart agriculture, based on today’s variable-rate technology, geospatial technology, sensor technology, Internet of Things, open-source data and algorithms, machine learning (e.g., deep learning), and high-performance computing can benefit from these opportunities and can address the new food production challenges related to cropping system optimization for improving productivity and reducing environmental impacts.

This special issue of Agronomy, entitled “Applications of Deep Learning in Smart Agriculture - Volume II” aims at presenting the state-of-the-art and original analytical methods based on deep learning for converging diverse advanced agro-environmental data from machinery, drone, airborne, and satellite sensors into information relevant to various agronomy sciences applications. Research papers that examine the latest developments in concepts, methods, techniques, and case study applications are welcomed.

Dr. Saeid Homayouni
Dr. Yacine Bouroubi
Prof. Dr. Karem Chokmani
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. Agronomy 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 2200 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
  • digital agriculture
  • precision agriculture
  • variable-rate technology
  • automatic agricultural screening
  • deep learning
  • computer vision
  • convolutional neural networks
  • recurrent neural networks
  • data mining
  • data analytics
  • big data
  • modeling
  • remote sensing (satellite, airborne, UAV Imagery, and proximal sensing)
  • crop monitoring and mapping
  • disease detection
  • phenological characterization
  • global positioning system and geospatial information technology
  • robotics
  • Internet of Things

Published Papers (2 papers)

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Research

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Article
Apple Leaf Disease Identification in Complex Background Based on BAM-Net
by
Yuxi Gao
,
Zhongzhu Cao
,
Weiwei Cai
,
Gufeng Gong
,
Guoxiong Zhou
and
Liujun Li
Agronomy 2023, 13(5), 1240; https://doi.org/10.3390/agronomy13051240 - 27 Apr 2023
Viewed by 459
Abstract
Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In [...] Read more.
Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture—Volume II)
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Article
Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm
by
Liyuan Cai
,
Jingming Liang
,
Xing Xu
,
Jieli Duan
and
Zhou Yang
Agronomy 2023, 13(4), 999; https://doi.org/10.3390/agronomy13040999 - 28 Mar 2023
Viewed by 899
Abstract
Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This [...] Read more.
Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This paper proposes an improved YOLOV7 deep learning object detection algorithm, YOLOV7-FM, for detecting banana pseudostems with different growth conditions. In the loss optimization part of the YOLOV7 model, Focal loss is introduced, to optimize the problematic training for banana pseudostems that are dense and sheltered, so as to improve the recognition rate of challenging samples. In the data augmentation part of the YOLOV7 model, the Mixup data augmentation is used, to improve the model’s generalization ability for banana pseudostems with similar features to complex environments. This paper compares the AP (average precision) and inference speed of the YOLOV7-FM algorithm with YOLOX, YOLOV5, YOLOV3, and Faster R-CNN algorithms. The results show that the AP and inference speed of the YOLOV7-FM algorithm is higher than those models that are compared, with an average inference time of 8.0 ms per image containing banana pseudostems and AP of 81.45%. This improved YOLOV7-FM model can achieve fast and accurate detection of banana pseudostems. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture—Volume II)
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