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: closed (31 August 2023) | Viewed by 10673

Special Issue Editors


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

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

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 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
  • 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 (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 4081 KiB  
Article
An Improved MobileNetV3 Mushroom Quality Classification Model Using Images with Complex Backgrounds
by Fengwu Zhu, Yan Sun, Yuqing Zhang, Weijian Zhang and Ji Qi
Agronomy 2023, 13(12), 2924; https://doi.org/10.3390/agronomy13122924 - 28 Nov 2023
Viewed by 905
Abstract
Shiitake mushrooms are an important edible fungus, and their nutrient content is related to their quality. With the acceleration of urbanization, there has been a serious loss of population and shortage of labor in rural areas. The problem of harvesting agricultural products after [...] Read more.
Shiitake mushrooms are an important edible fungus, and their nutrient content is related to their quality. With the acceleration of urbanization, there has been a serious loss of population and shortage of labor in rural areas. The problem of harvesting agricultural products after maturity is becoming more and more prominent. In recent years, deep learning techniques have performed well in classification tasks using image data. These techniques can replace the manual labor needed to classify the quality of shiitake mushrooms quickly and accurately. Therefore, in this paper, a MobileNetV3_large deep convolutional network is improved, and a mushroom quality classification model using images with complex backgrounds is proposed. First, captured image data of shiitake mushrooms are divided into three categories based on the appearance characteristics related to shiitake quality. By constructing a hybrid data set, the model’s focus on shiitake mushrooms in images with complex backgrounds is improved. And the constructed data set is expanded using data enhancement methods to improve the generalization ability of the model. The total number of images after expansion is 10,991. Among them, the number of primary mushroom images is 3758, the number of secondary mushroom images is 3678, and the number of tertiary mushroom images is 3555. Subsequently, the SE module in MobileNetV3_large network is improved and processed to enhance the model recognition accuracy while reducing the network size. Finally, PolyFocalLoss and migration learning strategies are introduced to train the model and accelerate model convergence. In this paper, the recognition performance of the improved MobileNetV3_large model is evaluated by using the confusion matrix evaluation tool. It is also compared with other deep convolutional network models such as VGG16, GoogLeNet, ResNet50, MobileNet, ShuffleNet, and EfficientNet using the same experimental conditions. The results show that the improved MobileNetV3_large network has a recognition accuracy of 99.91%, a model size of 11.9 M, and a recognition error rate of 0.09% by the above methods. Compared to the original model, the recognition accuracy of the improved model is increased by 18.81% and the size is reduced by 26.54%. The improved MobileNetV3_large network model in this paper has better comprehensive performance, and it can provide a reference for the development of quality recognition and classification technologies for shiitake mushrooms cultivated in greenhouse environments. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture—Volume II)
Show Figures

Figure 1

15 pages, 3648 KiB  
Article
Collaborative Wheat Lodging Segmentation Semi-Supervised Learning Model Based on RSE-BiSeNet Using UAV Imagery
by Hongbo Zhi, Baohua Yang and Yue Zhu
Agronomy 2023, 13(11), 2772; https://doi.org/10.3390/agronomy13112772 - 06 Nov 2023
Viewed by 826
Abstract
Lodging is a common natural disaster during wheat growth. The accurate identification of wheat lodging is of great significance for early warnings and post-disaster assessment. With the widespread use of unmanned aerial vehicles (UAVs), large-scale wheat lodging monitoring has become very convenient. In [...] Read more.
Lodging is a common natural disaster during wheat growth. The accurate identification of wheat lodging is of great significance for early warnings and post-disaster assessment. With the widespread use of unmanned aerial vehicles (UAVs), large-scale wheat lodging monitoring has become very convenient. In particular, semantic segmentation is widely used in the recognition of high-resolution field scene images from UAVs, providing a new technical path for the accurate identification of wheat lodging. However, there are still problems, such as insufficient wheat lodging data, blurred image edge information, and the poor accuracy of small target feature extraction, which limit the recognition of wheat lodging. To this end, the collaborative wheat lodging segmentation semi-supervised learning model based on RSE-BiseNet is proposed in this study. Firstly, ResNet-18 was used in the context path of BiSeNet to replace the original backbone network and introduce squeeze-and-excitation (SE) attention, aiming to enhance the expression ability of wheat lodging characteristics. Secondly, the segmentation effects of the collaborative semi-supervised and fully supervised learning model based on RSE-BiSeNet were compared using the self-built wheat lodging dataset. Finally, the test results of the proposed RSE-BiSeNet model were compared with classic network models such as U-Net, BiseNet, and DeepLabv3+. The experimental results showed that the wheat lodging segmentation model based on RSE-BiSeNet collaborative semi-supervised learning has a good performance. The method proposed in this study can also provide references for remote sensing UAVs, other field crop disaster evaluations, and production assistance. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture—Volume II)
Show Figures

Figure 1

24 pages, 8774 KiB  
Article
A Lightweight Cherry Tomato Maturity Real-Time Detection Algorithm Based on Improved YOLOV5n
by Congyue Wang, Chaofeng Wang, Lele Wang, Jing Wang, Jiapeng Liao, Yuanhong Li and Yubin Lan
Agronomy 2023, 13(8), 2106; https://doi.org/10.3390/agronomy13082106 - 11 Aug 2023
Cited by 4 | Viewed by 1896
Abstract
To enhance the efficiency of mechanical automatic picking of cherry tomatoes in a precision agriculture environment, this study proposes an improved target detection algorithm based on YOLOv5n. The improvement steps are as follows: First, the K-means++ clustering algorithm is utilized to update the [...] Read more.
To enhance the efficiency of mechanical automatic picking of cherry tomatoes in a precision agriculture environment, this study proposes an improved target detection algorithm based on YOLOv5n. The improvement steps are as follows: First, the K-means++ clustering algorithm is utilized to update the scale and aspect ratio of the anchor box, adapting it to the shape characteristics of cherry tomatoes. Secondly, the coordinate attention (CA) mechanism is introduced to expand the receptive field range and reduce interference from branches, dead leaves, and other backgrounds in the recognition of cherry tomato maturity. Next, the traditional loss function is replaced by the bounding box regression loss with dynamic focusing mechanism (WIoU) loss function. The outlier degree and dynamic nonmonotonic focusing mechanism are introduced to address the boundary box regression balance problem between high-quality and low-quality data. This research employs a self-built cherry tomato dataset to train the target detection algorithms before and after the improvements. Comparative experiments are conducted with YOLO series algorithms. The experimental results indicate that the improved model has achieved a 1.4% increase in both precision and recall compared to the previous model. It achieves an average accuracy mAP of 95.2%, an average detection time of 5.3 ms, and a weight file size of only 4.4 MB. These results demonstrate that the model fulfills the requirements for real-time detection and lightweight applications. It is highly suitable for deployment in embedded systems and mobile devices. The improved model presented in this paper enables real-time target recognition and maturity detection for cherry tomatoes. It provides rapid and accurate target recognition guidance for achieving mechanical automatic picking of cherry tomatoes. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture—Volume II)
Show Figures

Figure 1

16 pages, 7670 KiB  
Article
Dense Papaya Target Detection in Natural Environment Based on Improved YOLOv5s
by Lei Wang, Hongcheng Zheng, Chenghai Yin, Yong Wang, Zongxiu Bai and Wei Fu
Agronomy 2023, 13(8), 2019; https://doi.org/10.3390/agronomy13082019 - 29 Jul 2023
Cited by 2 | Viewed by 1129
Abstract
Due to the fact that the green features of papaya skin are the same colour as the leaves, the dense growth of fruits causes serious overlapping occlusion phenomenon between them, which increases the difficulty of target detection by the robot during the picking [...] Read more.
Due to the fact that the green features of papaya skin are the same colour as the leaves, the dense growth of fruits causes serious overlapping occlusion phenomenon between them, which increases the difficulty of target detection by the robot during the picking process. This study proposes an improved YOLOv5s-Papaya deep convolutional neural network for achieving dense multitarget papaya detection in natural orchard environments. The model is based on the YOLOv5s network architecture and incorporates the Ghost module to enhance its lightweight characteristics. The Ghost module employs a strategy of grouped convolutional layers and weighted fusion, allowing for more efficient feature representation and improved model performance. A coordinate attention module is introduced to improve the accuracy of identifying dense multitarget papayas. The fusion of bidirectional weighted feature pyramid networks in the PANet structure of the feature fusion layer enhances the performance of papaya detection at different scales. Moreover, the scaled intersection over union bounding box regression loss function is used rather than the complete intersection over union bounding box regression loss function to enhance the localisation accuracy of dense targets and expedite the convergence of the network model training. Experimental results show that the YOLOv5s-Papaya model achieves detection average precision, precision, and recall rates of 92.3%, 90.4%, and 83.4%, respectively. The model’s size, number of parameters, and floating-point operations are 11.5 MB, 6.2 M, and 12.8 G, respectively. Compared to the original YOLOv5s network model, the model detection average precision is improved by 3.6 percentage points, the precision is improved by 4.3 percentage points, the number of parameters is reduced by 11.4%, and the floating-point operations are decreased by 18.9%. The improved model has a lighter structure and better detection performance. This study provides the theoretical basis and technical support for intelligent picking recognition of overlapping and occluded dense papayas in natural environments. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture—Volume II)
Show Figures

Figure 1

28 pages, 8702 KiB  
Article
Research and Explainable Analysis of a Real-Time Passion Fruit Detection Model Based on FSOne-YOLOv7
by Juji Ou, Rihong Zhang, Xiaomin Li and Guichao Lin
Agronomy 2023, 13(8), 1993; https://doi.org/10.3390/agronomy13081993 - 27 Jul 2023
Cited by 1 | Viewed by 1331
Abstract
Real-time object detection plays an indispensable role in facilitating the intelligent harvesting process of passion fruit. Accordingly, this paper proposes an FSOne-YOLOv7 model designed to facilitate the real-time detection of passion fruit. The model addresses the challenges arising from the diverse appearance characteristics [...] Read more.
Real-time object detection plays an indispensable role in facilitating the intelligent harvesting process of passion fruit. Accordingly, this paper proposes an FSOne-YOLOv7 model designed to facilitate the real-time detection of passion fruit. The model addresses the challenges arising from the diverse appearance characteristics of passion fruit in complex growth environments. An enhanced version of the YOLOv7 architecture serves as the foundation for the FSOne-YOLOv7 model, with ShuffleOne serving as the novel backbone network and slim-neck operating as the neck network. These architectural modifications significantly enhance the capabilities of feature extraction and fusion, thus leading to improved detection speed. By utilizing the explainable gradient-weighted class activation mapping technique, the output features of FSOne-YOLOv7 exhibit a higher level of concentration and precision in the detection of passion fruit compared to YOLOv7. As a result, the proposed model achieves more accurate, fast, and computationally efficient passion fruit detection. The experimental results demonstrate that FSOne-YOLOv7 outperforms the original YOLOv7, exhibiting a 4.6% increase in precision (P) and a 4.85% increase in mean average precision (mAP). Additionally, it reduces the parameter count by approximately 62.7% and enhances real-time detection speed by 35.7%. When compared to Faster-RCNN and SSD, the proposed model exhibits a 10% and 4.4% increase in mAP, respectively, while achieving approximately 2.6 times and 1.5 times faster real-time detection speeds, respectively. This model proves to be particularly suitable for scenarios characterized by limited memory and computing capabilities where high accuracy is crucial. Moreover, it serves as a valuable technical reference for passion fruit detection applications on mobile or embedded devices and offers insightful guidance for real-time detection research involving similar fruits. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture—Volume II)
Show Figures

Figure 1

22 pages, 5144 KiB  
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
Cited by 5 | Viewed by 1417
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)
Show Figures

Figure 1

20 pages, 14697 KiB  
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
Cited by 5 | Viewed by 2201
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)
Show Figures

Figure 1

Back to TopTop