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Agriculture
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Hardware and Software Support for Insect Pest Management
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Hardware and Software Support for Insect Pest Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 9316

Special Issue Editor

Dr. József Sütő

E-Mail Website
Guest Editor
1. Department of IT Systems and Networks, University of Debrecen, 4028 Debrecen, Hungary
2. Department of IT, Eszterházy Károly Catholic University, 3300 Eger, Hungary
Interests: AI in embedded systems; AI for computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Flying insect pest detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing.

The conventional monitoring approach of insect swarming is based on traps that are checked by human operators periodically. However, with the aid of machine vision, machine learning, and modern integrated circuit technology, the traditional identification and counting process can be automated.

To achieve this goal, a particular data acquisition device and an accurate insect detector algorithm (model) are necessary, which enable insect swarm prediction. With the appropriate combination of hardware and software components, an intervention (e.g., spraying) can be accurately scheduled, and the crop defending cost will be significantly reduced.

The aim of this Special Issue is to encourage researchers to present original hardware and software developments that make insect pest management even more efficient.

Dr. József Sütő
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. Agriculture 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 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

  • automated trap
  • pest counting
  • pest management
  • pest monitoring
  • precision agriculture
  • remote sensing

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Published Papers (6 papers)

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Editorial

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2 pages, 173 KiB  
Editorial
Hardware and Software Support for Insect Pest Management
by Jozsef Suto
Agriculture 2023, 13(9), 1818; https://doi.org/10.3390/agriculture13091818 - 16 Sep 2023
Cited by 2 | Viewed by 1042
Abstract
In recent years, the achievements of machine learning (ML) have affected all areas of industry and it plays an increasingly important role in agriculture as well [...] Full article
(This article belongs to the Special Issue Hardware and Software Support for Insect Pest Management)

Research

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20 pages, 4137 KiB  
Article
EfficientDet-4 Deep Neural Network-Based Remote Monitoring of Codling Moth Population for Early Damage Detection in Apple Orchard
by Dana Čirjak, Ivan Aleksi, Darija Lemic and Ivana Pajač Živković
Agriculture 2023, 13(5), 961; https://doi.org/10.3390/agriculture13050961 - 26 Apr 2023
Cited by 11 | Viewed by 2741
Abstract
Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time-consuming. Therefore, the aim of this study [...] Read more.
Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time-consuming. Therefore, the aim of this study was to develop an automatic monitoring system for codling moth based on DNNs. The system consists of a smart trap and an analytical model. The smart trap enables data processing on-site and does not send the whole image to the user but only the detection results. Therefore, it does not consume much energy and is suitable for rural areas. For model development, a dataset of 430 sticky pad photos of codling moth was collected in three apple orchards. The photos were labelled, resulting in 8142 annotations of codling moths, 5458 of other insects, and 8177 of other objects. The results were statistically evaluated using the confusion matrix, and the developed model showed an accuracy > of 99% in detecting codling moths. This developed system contributes to automatic pest monitoring and sustainable apple production. Full article
(This article belongs to the Special Issue Hardware and Software Support for Insect Pest Management)
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18 pages, 2626 KiB  
Article
Predicting the Occurrence and Risk Damage Caused by the Two-Spotted Spider Mite Tetranychus urticae (Koch) in Dry Beans (Phaseolus vulgaris L.) Combining Rate and Heat Summation Models for Digital Decisions Support
by Petros Damos, Fokion Papathanasiou, Evaggelos Tsikos, Thomas Kyriakidis and Malamati Louta
Agriculture 2023, 13(4), 756; https://doi.org/10.3390/agriculture13040756 - 24 Mar 2023
Cited by 3 | Viewed by 1954
Abstract
In this work, we use developmental rate models to predict egg laying activity and succession of generations of T. urticae populations under field conditions in the Prespa lakes region in Northern Greece. Moreover, the developmental rate model predictions are related to accumulated heat [...] Read more.
In this work, we use developmental rate models to predict egg laying activity and succession of generations of T. urticae populations under field conditions in the Prespa lakes region in Northern Greece. Moreover, the developmental rate model predictions are related to accumulated heat summations to be compared with actual bean damage and to generate pest-specific degree-day risk thresholds. The oviposition was predicted to start at 57.7 DD, while the first peak in egg laying was estimated to be at 141.8 DD. The second and third peak in egg production were predicted to occur at 321.1 and 470.5 DD, respectively. At the degree-day risk threshold, half development of the first summer generation was estimated at 187 DD and 234 DDm while for the second, it was estimated at 505 DD and 547 DD for 2021 and 2022, respectively. According to the model predictions, no significant differences were observed in the mean generation time (total egg to adult development) of T. urticae between the two observation years (t = 0.01, df = 15, p = 0.992). The total generation time was estimated at 249.3 (±7.7) and 249.2 (±6.7), for 2021 and 2022, respectively. The current models will contribute towards predictions of the seasonal occurrence and oviposition of T. urticae to be used in pest management decision-making. Moreover, the development of population model is a prerequisite for the buildup and implementation of smart plant protection solutions. Full article
(This article belongs to the Special Issue Hardware and Software Support for Insect Pest Management)
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16 pages, 6321 KiB  
Article
YOLO-Based Light-Weight Deep Learning Models for Insect Detection System with Field Adaption
by Nithin Kumar, Nagarathna and Francesco Flammini
Agriculture 2023, 13(3), 741; https://doi.org/10.3390/agriculture13030741 - 22 Mar 2023
Cited by 25 | Viewed by 8203
Abstract
The most incredible diversity, abundance, spread, and adaptability in biology are found in insects. The foundation of insect study and pest management is insect recognition. However, most of the current insect recognition research depends on a small number of insect taxonomic experts. We [...] Read more.
The most incredible diversity, abundance, spread, and adaptability in biology are found in insects. The foundation of insect study and pest management is insect recognition. However, most of the current insect recognition research depends on a small number of insect taxonomic experts. We can use computers to differentiate insects accurately instead of professionals because of the quick advancement of computer technology. The “YOLOv5” model, with five different state of the art object detection techniques, has been used in this insect recognition and classification investigation to identify insects with the subtle differences between subcategories. To enhance the critical information in the feature map and weaken the supporting information, both channel and spatial attention modules are introduced, improving the network’s capacity for recognition. The experimental findings show that the F1 score approaches 0.90, and the mAP value reaches 93% through learning on the self-made pest dataset. The F1 score increased by 0.02, and the map increased by 1% as compared to other YOLOv5 models, demonstrating the success of the upgraded YOLOv5-based insect detection system. Full article
(This article belongs to the Special Issue Hardware and Software Support for Insect Pest Management)
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16 pages, 2764 KiB  
Article
A Novel Plug-in Board for Remote Insect Monitoring
by Jozsef Suto
Agriculture 2022, 12(11), 1897; https://doi.org/10.3390/agriculture12111897 - 11 Nov 2022
Cited by 11 | Viewed by 2988
Abstract
The conventional approach to monitoring insect swarming is based on traps that are periodically checked by human operators. However, human checking of trap contents is expensive, and in many environments, the pest species most frequently encountered in the traps can be detected and [...] Read more.
The conventional approach to monitoring insect swarming is based on traps that are periodically checked by human operators. However, human checking of trap contents is expensive, and in many environments, the pest species most frequently encountered in the traps can be detected and monitored automatically. To achieve this goal, a dedicated data acquisition device is necessary, which makes real-time and online pest monitoring possible from a distant location. In addition, it is beneficial for the device to run machine learning algorithms that count and identify insects automatically from pictures. Thanks to the advantages of integrated circuits, more systems have been designed to improve integrated pest management in the context of precision agriculture. However, in our opinion, all of those systems have one or more disadvantages, such as high cost, low power autonomy, low picture quality, a WIFI coverage requirement, intensive human control, and poor software support. Therefore, the aim of this work is to present a novel plug-in board for automatic pest detection and counting. The plug-in board is dedicated to Raspberry Pi devices, especially the Raspberry Pi Zero. The proposed board, in combination with a Raspberry Pi device and a Pi camera, overcomes the limitations of other prototypes found in the literature. In this paper, a detailed description can be found about the schematic and characteristics of the board with a deep-learning-based insect-counting method. Full article
(This article belongs to the Special Issue Hardware and Software Support for Insect Pest Management)
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Review

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18 pages, 2153 KiB  
Review
Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review
by Jozsef Suto
Agriculture 2022, 12(10), 1721; https://doi.org/10.3390/agriculture12101721 - 19 Oct 2022
Cited by 19 | Viewed by 4458
Abstract
The codling moth (Cydia pomonella) is probably the most harmful pest in apple and pear orchards. The crop loss due to the high harmfulness of the insect can be extremely expensive; therefore, sophisticated pest management is necessary to protect the crop. [...] Read more.
The codling moth (Cydia pomonella) is probably the most harmful pest in apple and pear orchards. The crop loss due to the high harmfulness of the insect can be extremely expensive; therefore, sophisticated pest management is necessary to protect the crop. The conventional monitoring approach for insect swarming has been based on traps that are periodically checked by human operators. However, this workflow can be automatized. To achieve this goal, a dedicated image capture device and an accurate insect counter algorithm are necessary which make online insect swarm prediction possible. From the hardware side, more camera-equipped embedded systems have been designed to remotely capture and upload pest trap images. From the software side, with the aid of machine vision and machine learning methods, traditional (manual) identification and counting can be solved by algorithm. With the appropriate combination of the hardware and software components, spraying can be accurately scheduled, and the crop-defending cost will be significantly reduced. Although automatic traps have been developed for more pest species and there are a large number of papers which investigate insect detection, a limited number of articles focus on the C. pomonella. The aim of this paper is to review the state of the art of C. pomonella monitoring with camera-equipped traps. The paper presents the advantages and disadvantages of automated traps’ hardware and software components and examines their practical applicability. Full article
(This article belongs to the Special Issue Hardware and Software Support for Insect Pest Management)
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