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AgriEngineering
Special Issues
Computer Vision for Agriculture and Smart Farming
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Computer Vision for Agriculture and Smart Farming

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 7192

Special Issue Editor

Dr. Mariano Crimaldi

E-Mail Website
Guest Editor
Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
Interests: precision agriculture; AI in agriculture; sensors; UAVs; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, computer vision as a non-destructive, non-contact image analysis technique has been used in a variety of application fields. The use of deep learning algorithms and artificial intelligence has enabled the enhancement and specialization of these techniques by also specializing them in the field of agriculture and smart farming. The use of computer vision allows the improvement of management, planning, prediction, and decision-making of every stage of agricultural processing and smart farming. The implementation of computer vision also enables the use of automated operators such as rovers and UAVs capable of recognizing the target and operating autonomously.

This Special Issue aims to bring together recent developments and applications of computer vision and artificial intelligence in the field of agriculture and smart farming as evidence that they can be applied to improve the management, prediction, planning and enforcement of all phases of agricultural and farming practices. Submissions are open for original scientific articles, reviews, and technical reports on the use of computer vision and artificial intelligence in disease, pest, and weed detection; crop growth monitoring; automatic crop harvesting; automated pesticide spraying; product inspection and quality testing; plant phenotyping; species recognition; yield prediction; water management; soil management; livestock, poultry, and fish farming. We would like to invite you to share with the broad audience of the journal AgriEngineering your experience in the research and development of computer vision applications and techniques for agriculture and smart farming. Papers presented in this Special Issue can build on the number of other published publications in this field. Your papers will enlarge the knowledge and skills of the scientific community in this area of expanding agricultural research and development.

Dr. Mariano Crimaldi
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • computer vision
  • artificial intelligence
  • deep learning
  • machine learning
  • smart agriculture
  • agriculture 4.0
  • neural networks
  • sensors
  • UAV
  • drones
  • autonomous agricultural operators
  • rovers
  • UTVs
  • livestock farming
  • IoT
  • remote sensing

Published Papers (3 papers)

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Research

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18 pages, 9158 KiB  
Article
A Novel Algorithm to Detect White Flowering Honey Trees in Mixed Forest Ecosystems Using UAV-Based RGB Imaging
by Atanas Z. Atanasov, Boris I. Evstatiev, Valentin N. Vladut and Sorin-Stefan Biris
AgriEngineering 2024, 6(1), 95-112; https://doi.org/10.3390/agriengineering6010007 - 11 Jan 2024
Viewed by 774
Abstract
Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study [...] Read more.
Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study is aimed at the creation of a novel algorithm to identify and distinguish white flowering honey plants, such as black locust (Robinia pseudo-acacia) and to determine the areas occupied by this forest species in mixed forest ecosystems using UAV-based RGB imaging. In our study, to determine the plant cover of black locust in mixed forest ecosystems we used a DJI (Da-Jiang Innovations, Shenzhen, China) Phantom 4 Multispectral drone with 6 multispectral cameras with 1600 × 1300 image resolution. The monitoring was conducted in the May 2023 growing season in the village of Yuper, Northeast Bulgaria. The geographical location of the experimental region is 43°32′4.02″ N and 25°45′14.10″ E at an altitude of 223 m. The UAV was used to make RGB and multispectral images of the investigated forest massifs, which were thereafter analyzed with the software product QGIS 3.0. The spectral images of the observed plants were evaluated using the newly created criteria for distinguishing white from non-white colors. The results obtained for the scanned area showed that approximately 14–15% of the area is categorized as white-flowered trees, and the remaining 86–85%—as non-white-flowered. The comparison of the developed algorithm with the Enhanced Bloom Index (EBI) approach and with supervised Support Vector Machine (SVM) classification showed that the suggested criterion is easy to understand for users with little technical experience, very accurate in identifying white blooming trees, and reduces the number of false positives and false negatives. The proposed approach of detecting and mapping the areas occupied by white flowering honey plants, such as black locust (Robinia pseudo-acacia) in mixed forest ecosystems is of great importance for beekeepers in determining the productive potential of the region and choosing a place for an apiary. Full article
(This article belongs to the Special Issue Computer Vision for Agriculture and Smart Farming)
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19 pages, 17534 KiB  
Article
Detection of Varroa destructor Infestation of Honeybees Based on Segmentation and Object Detection Convolutional Neural Networks
by Mochen Liu, Mingshi Cui, Baohua Xu, Zhenguo Liu, Zhenghao Li, Zhenyuan Chu, Xinshan Zhang, Guanlu Liu, Xiaoli Xu and Yinfa Yan
AgriEngineering 2023, 5(4), 1644-1662; https://doi.org/10.3390/agriengineering5040102 - 26 Sep 2023
Cited by 1 | Viewed by 1264
Abstract
Varroa destructor infestation is a major factor leading to the global decline of honeybee populations. Monitoring the level of Varroa mite infestation in order to take timely control measures is crucial for the protection of bee colonies. Machine vision systems can achieve non-invasive [...] Read more.
Varroa destructor infestation is a major factor leading to the global decline of honeybee populations. Monitoring the level of Varroa mite infestation in order to take timely control measures is crucial for the protection of bee colonies. Machine vision systems can achieve non-invasive Varroa mite detection on bee colonies, but it is challenged by two factors: the complex dynamic scenes of honeybees and small-scale and limited data on Varroa destructor. We design a convolutional neural network integrated with machine vision to solve these problems. To address the first challenge, we separate the image of the honeybee from its surroundings using a segmentation network, and the object-detection network YOLOX detects Varroa mites within the segmented regions. This collaboration between segmentation and object detection allows for more precise detection and reduces false positives. To handle the second challenge, we add a Coordinate Attention (CA) mechanism in YOLOX to extract a more discriminative representation of Varroa destructor and improve the confidence loss function to alleviate the problem of class imbalance. The experimental results in the bee farm showed that the evaluation metrics of our model are better than other models. Our network’s detection value for the percentage of honeybees infested with Varroa mites is 1.13%, which is the closest to the true value of 1.19% among all the detection values. Full article
(This article belongs to the Special Issue Computer Vision for Agriculture and Smart Farming)
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Review

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20 pages, 666 KiB  
Review
Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture
by Jaemyung Shin, Md. Sultan Mahmud, Tanzeel U. Rehman, Prabahar Ravichandran, Brandon Heung and Young K. Chang
AgriEngineering 2023, 5(1), 20-39; https://doi.org/10.3390/agriengineering5010003 - 24 Dec 2022
Cited by 11 | Viewed by 3820
Abstract
Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation [...] Read more.
Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation can improve productivity and quality by reducing errors and adding flexibility to the work process. Primarily, machine vision technology has been used to develop crop production systems by detecting diseases more efficiently. This review provides a comprehensive overview of machine vision applications for stress/disease detection on crops, leaves, fruits, and vegetables with an exploration of new technology trends as well as the future expectation in precision agriculture. In conclusion, research on the advanced machine vision system is expected to develop the overall agricultural management system and provide rich recommendations and insights into decision-making for farmers. Full article
(This article belongs to the Special Issue Computer Vision for Agriculture and Smart Farming)
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