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Electronics
Special Issues
Machine Vision and 3D Sensing in Smart Agriculture
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Machine Vision and 3D Sensing in Smart Agriculture

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 3746

Special Issue Editors

Dr. Lu Liu

E-Mail Website
Guest Editor
College of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: smart agriculture; intelligent agricultural equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Yuwei Wang

E-Mail Website
Guest Editor
College of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: machine vision; optical measurement; smart agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Wenhui Hou

E-Mail Website
Guest Editor
College of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: smart agriculture; deep learning; machine vision

Special Issue Information

Dear Colleagues,

In recent decades, machine vision and 3D sensing technologies have enabled precision agriculture practices. This Special Issue focuses on agricultural problems, machine vision, and 3D sensing techniques applied to smart or precision agriculture for plant phenotyping, crop monitoring, stress detection, yield prediction, fruit or vegetable quality detection, etc. Traditional and advanced sensing technologies, including visible imaging, near-infrared image, multispectral imaging, hyperspectral imaging, remote sensing, structured light, laser radar, and binocular vision, are increasingly used for various agricultural applications. The Special Issue highlights these technologies for increasing agricultural productivity, reducing resource wastage, and mitigating environmental impacts. Overall, the Special Issue provides valuable insights into the current status and future directions of machine vision and 3D sensing in smart agriculture.

Dr. Lu Liu
Dr. Yuwei Wang
Dr. Wenhui Hou
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • machine vision
  • 3D sensing
  • smart agriculture
  • image processing
  • deep learning
  • plant phenotyping
  • crop monitoring
  • fruit and vegetable grading

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Published Papers (3 papers)

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Research

15 pages, 3247 KiB  
Article
Design and Experiments of a Convex Curved Surface Type Grain Yield Monitoring System
by Yijun Fang, Zhijian Chen, Luning Wu, Sheikh Muhammad Farhan, Maile Zhou and Jianjun Yin
Electronics 2024, 13(2), 254; https://doi.org/10.3390/electronics13020254 - 5 Jan 2024
Cited by 1 | Viewed by 930
Abstract
Precision agriculture relies heavily on measuring grain production per unit plot, and a grain flow monitoring system performs this using a combine harvester. In response to the high cost, complex structure, and low stability of the yield monitoring system for grain combine harvesters, [...] Read more.
Precision agriculture relies heavily on measuring grain production per unit plot, and a grain flow monitoring system performs this using a combine harvester. In response to the high cost, complex structure, and low stability of the yield monitoring system for grain combine harvesters, the objective of this research was to design a convex curved grain mass flow sensor to improve the accuracy and practicality of grain yield monitoring. In addition, it involves the development of a grain yield monitoring system based on a cut-and-flow combine harvester prototype. This research examined the real output signal of the convex curved grain mass flow sensor. Errors caused by variations in terrain were reduced by establishing the zero point of the sensor’s output. Measurement errors under different material characteristics, flow rates, and grain types were compared in indoor experiments, and the results were subsequently confirmed through field experiments. The results showed that a sensor with a cantilever beam-type elastic element and a well-constructed carrier plate may achieve a measurement error of less than 5%. After calibrating the sensor’s zero and factors, it demonstrated a measurement error of less than 5% during the operation of the combine harvester. These experimental results align with the expected results and can provide valuable technical support for the widespread adoption of impulse grain flow detection technology. In future work, the impact of factors such as vehicle vibration will be addressed, and system accuracy will be improved through structural design or adaptive filtering processing to promote the commercialization of the system. Full article
(This article belongs to the Special Issue Machine Vision and 3D Sensing in Smart Agriculture)
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13 pages, 2549 KiB  
Article
A Binary Neural Network with Dual Attention for Plant Disease Classification
by Ping Ma, Junan Zhu and Gan Zhang
Electronics 2023, 12(21), 4431; https://doi.org/10.3390/electronics12214431 - 27 Oct 2023
Viewed by 1057
Abstract
Plant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-performance devices to [...] Read more.
Plant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-performance devices to meet the requirements for classification accuracy. However, agricultural applications have strict cost control and cannot be widely promoted. This paper presents a novel method for plant disease classification using a binary neural network with dual attention (DABNN), which can save computational resources and accelerate by using binary neural networks, and introduces a dual-attention mechanism to improve the accuracy of classification. To evaluate the effectiveness of our proposed approach, we conduct experiments on the PlantVillage dataset, which includes a range of diseases. The F1score and Accuracy of our method reach 99.39% and 99.4%, respectively. Meanwhile, compared to AlexNet and VGG16, the Computationalcomplexity of our method is reduced by 72.3% and 98.7%, respectively. The Paramssize of our algorithm is 5.4% of AlexNet and 2.3% of VGG16. The experimental results show that DABNN can identify various diseases effectively and accurately. Full article
(This article belongs to the Special Issue Machine Vision and 3D Sensing in Smart Agriculture)
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19 pages, 10299 KiB  
Article
Pattern Orientation Finder (POF): A Robust, Bio-Inspired Light Algorithm for Pattern Orientation Measurement
by Alessandro Carlini and Michel Paindavoine
Electronics 2023, 12(20), 4354; https://doi.org/10.3390/electronics12204354 - 20 Oct 2023
Viewed by 1043
Abstract
We present the Pattern Orientation Finder (POF), an innovative, bio-inspired algorithm for measuring the orientation of patterns of parallel elements. The POF was developed to obtain an autonomous navigation system for drones inspecting vegetable cultivations. The main challenge was to obtain an accurate [...] Read more.
We present the Pattern Orientation Finder (POF), an innovative, bio-inspired algorithm for measuring the orientation of patterns of parallel elements. The POF was developed to obtain an autonomous navigation system for drones inspecting vegetable cultivations. The main challenge was to obtain an accurate and reliable measurement of orientation despite the high level of noise that characterizes aerial views of vegetable crops. The POF algorithm is computationally light and operable on embedded systems. We assessed the performance of the POF algorithm using images of different cultivation types. The outcomes were examined in light of the accuracy and reliability of the measurement; special attention was paid to the relationship between performance and parameterization. The results show that the POF guarantees excellent performance, even in more challenging conditions. The POF shows high reliability and robustness, even in high-noise contexts. Finally, tests on images from different sectors suggest that the POF has excellent potential for application to other fields as well. Full article
(This article belongs to the Special Issue Machine Vision and 3D Sensing in Smart Agriculture)
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