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Agronomy
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Application of Image Recognition Processing Technology in Agricultural
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Application of Image Recognition Processing Technology in Agricultural

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

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 12074

Special Issue Editors

Prof. Miguel Ángel Miranda Chueca

E-Mail Website
Guest Editor
Applied Zoology and Animal Conservation Research Group, University of the Balearic Islands, INAGEA-UIB, 07122 Palma, Spain
Interests: precision agriculture; image processing; Zoology
Dr. Bartomeu Alorda Ladaria

E-Mail Website
Guest Editor
Grupo de Sistemas Electrónicos, Departamento de Física, University of the Balearic Islands, 07122 Palma, Spain
Interests: agro-food; environmental sector; precision agriculture; image processing;

Special Issue Information

Dear Colleagues,

Computer technologies have been shown to improve agricultural productivity in a number of ways. One technique which is emerging as a useful tool is image processing. This Special Issue wants to explore the most advanced applications using image processing techniques combined with edge computing methodologies to assist researchers and farmers in improving agricultural practices. Image processing has been used to assist with precision agriculture practices, weed and herbicide technologies, monitoring plant growth, plant pest control, and plant nutrition management. Therefore, introducing edge computing capabilities in the field can impact the opportunities to overcome fast or local events such as insect pest movement. This special issue will highlight the challenges of future potential for image processing for different agricultural industry, environmental contexts.

Prof. Miguel Ángel Miranda Chueca
Dr. Bartomeu Alorda Ladaria
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

  • precision agriculture
  • image processing
  • edge computing
  • agro-environmental practices
  • computer vision applications
  • edge intelligence applications

Published Papers (2 papers)

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Research

15 pages, 5657 KiB  
Article
Tomato Maturity Classification Based on SE-YOLOv3-MobileNetV1 Network under Nature Greenhouse Environment
by Fei Su, Yanping Zhao, Guanghui Wang, Pingzeng Liu, Yinfa Yan and Linlu Zu
Agronomy 2022, 12(7), 1638; https://doi.org/10.3390/agronomy12071638 - 8 Jul 2022
Cited by 22 | Viewed by 5215
Abstract
The maturity level of tomato is a key factor of tomato picking, which directly determines the transportation distance, storage time, and market freshness of postharvest tomato. In view of the lack of studies on tomato maturity classification under nature greenhouse environment, this paper [...] Read more.
The maturity level of tomato is a key factor of tomato picking, which directly determines the transportation distance, storage time, and market freshness of postharvest tomato. In view of the lack of studies on tomato maturity classification under nature greenhouse environment, this paper proposes a SE-YOLOv3-MobileNetV1 network to classify four kinds of tomato maturity. The proposed maturity classification model is improved in terms of speed and accuracy: (1) Speed: Depthwise separable convolution is used. (2) Accuracy: Mosaic data augmentation, K-means clustering algorithm, and the Squeeze-and-Excitation attention mechanism module are used. To verify the detection performance, the proposed model is compared with the current mainstream models, such as YOLOv3, YOLOv3-MobileNetV1, and YOLOv5 in terms of accuracy and speed. The SE-YOLOv3-MobileNetV1 model is able to distinguish tomatoes in four kinds of maturity, the mean average precision value of tomato reaches 97.5%. The detection speed of the proposed model is 278.6 and 236.8 ms faster than the YOLOv3 and YOLOv5 model. In addition, the proposed model is considerably lighter than YOLOv3 and YOLOv5, which meets the need of embedded development, and provides a reference for tomato maturity classification of tomato harvesting robot. Full article
(This article belongs to the Special Issue Application of Image Recognition Processing Technology in Agricultural)
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24 pages, 12016 KiB  
Article
Deep-Learning-Based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images
by Adel Ammar, Anis Koubaa and Bilel Benjdira
Agronomy 2021, 11(8), 1458; https://doi.org/10.3390/agronomy11081458 - 22 Jul 2021
Cited by 35 | Viewed by 6011
Abstract
In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI [...] Read more.
In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI drones, and we built a dataset of around 11,000 instances of palm trees. Then, we applied several recent convolutional neural network models (Faster R-CNN, YOLOv3, YOLOv4, and EfficientDet) to detect palms and other trees, and we conducted a complete comparative evaluation in terms of average precision and inference speed. YOLOv4 and EfficientDet-D5 yielded the best trade-off between accuracy and speed (up to 99% mean average precision and 7.4 FPS). Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to automatically detect the geographical location of detected palm trees. This geolocation technique was tested on two different types of drones (DJI Mavic Pro and Phantom 4 pro) and was assessed to provide an average geolocation accuracy that attains 1.6 m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, this innovative combination between deep learning object detection and geolocalization can be generalized to any other objects in UAV images. Full article
(This article belongs to the Special Issue Application of Image Recognition Processing Technology in Agricultural)
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