Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Pest and Disease Management".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 6144

Special Issue Editors


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Guest Editor
Department of Entomology, VKS College of Agriculture, Dumraon, Bihar Agricultural University, Bihar-802136, India
Interests: insect pest management; insect population genetics; IPM; fruit fly ecology and management

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Guest Editor
Wageningen Plant Research, Wageningen University, Wageningen, The Netherlands
Interests: integrated pest management; machine vision; deep learning; biomonitoring

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Guest Editor
Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Interests: deep learning; machine learning; computer vision; robotics; smart agriculture

Special Issue Information

Dear Colleagues,

Changing climatic patterns have caused direct impacts on agroecosystems, resulting in increased food insecurity and poverty. Insect pests and diseases are the major challenges to sustaining crop yield. Up to date, the major bottleneck in pest and disease management is lack of reliable data. Effective and timely monitoring of insect pests and diseases is an essential component for the data-driven and sustainable pest and disease management in agroecosystems. The technological and analytical advances in monitoring tools and techniques have allowed a better understanding of pests and diseases.

This special issue aims to provide an overview of state-of-the-art and to bring together research communities that are working on the management of various pests and diseases in agroecosystems. This issue will also help in increasing awareness among pest management workers and experts so that data-driven and sustainable pest management can be achieved. This special issue welcomes, but is not limited to, manuscripts that address the following topics:

  • Techniques in computer vision, data science, or artificial intelligence for insect pest and plant disease monitoring;
  • Autonomous or mobile solutions (i.e., robots, unmanned ground/aerial vehicles, smart phones, wireless sensors, etc.) for pest and disease monitoring in agroecosystems;
  • Models for insect pest and diseases monitoring and forecasting.

Dr. Chandra Shekhar Prabhakar
Dr. Dan Jeric Arcega Rustia
Dr. Alvaro Fuentes
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. 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

  • insect pest
  • plant diseases
  • monitoring
  • artificial intelligence
  • agriculture
  • crops
  • climate change
  • pest populations

Published Papers (4 papers)

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Research

16 pages, 40315 KiB  
Article
Rapid Automatic Cacao Pod Borer Detection Using Edge Computing on Low-End Mobile Devices
by Eros Allan Somo Hacinas, Lorenzo Sangco Querol, Kris Lord T. Santos, Evian Bless Matira, Rhodina C. Castillo, Mercedes Arcelo, Divina Amalin and Dan Jeric Arcega Rustia
Agronomy 2024, 14(3), 502; https://doi.org/10.3390/agronomy14030502 - 29 Feb 2024
Viewed by 1453
Abstract
The cacao pod borer (CPB) (Conopomorpha cramerella) is an invasive insect that causes significant economic loss for cacao farmers. One of the most efficient ways to reduce CPB damage is to continuously monitor its presence. Currently, most automated technologies for continuous [...] Read more.
The cacao pod borer (CPB) (Conopomorpha cramerella) is an invasive insect that causes significant economic loss for cacao farmers. One of the most efficient ways to reduce CPB damage is to continuously monitor its presence. Currently, most automated technologies for continuous insect pest monitoring rely on an internet connection and a power source. However, most cacao plantations are remotely located and have limited access to internet and power sources; therefore, a simpler and readily available tool is necessary to enable continuous monitoring. This research proposes a mobile application developed for rapid and on-site counting of CPBs on sticky paper traps. A CPB counting algorithm was developed and optimized to enable on-device computations despite memory constraints and limited capacity of low-end mobile phones. The proposed algorithm has an F1-score of 0.88, with no significant difference from expert counts (R2 = 0.97, p-value = 0.55, α = 0.05). The mobile application can be used to provide the required information for pest control methods on-demand and is also accessible for low-income farms. This is one of the first few works on enabling on-device processing for insect pest monitoring. Full article
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13 pages, 5116 KiB  
Article
Classification of Plant Leaf Disease Recognition Based on Self-Supervised Learning
by Yuzhi Wang, Yunzhen Yin, Yaoyu Li, Tengteng Qu, Zhaodong Guo, Mingkang Peng, Shujie Jia, Qiang Wang, Wuping Zhang and Fuzhong Li
Agronomy 2024, 14(3), 500; https://doi.org/10.3390/agronomy14030500 - 28 Feb 2024
Viewed by 1111
Abstract
Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the [...] Read more.
Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the application of self-supervised learning (SSL) in plant disease recognition. We propose a new model that combines a masked autoencoder (MAE) and a convolutional block attention module (CBAM) to alleviate the harsh requirements of large amounts of labeled data. The performance of the model was validated on the CCMT dataset and our collected dataset. The results show that the improved model achieves an accuracy of 95.35% and 99.61%, recall of 96.2% and 98.51%, and F1 values of 95.52% and 98.62% on the CCMT dataset and our collected dataset, respectively. Compared with ResNet50, ViT, and MAE, the accuracies on the CCMT dataset improved by 1.2%, 0.7%, and 0.8%, respectively, and the accuracy of our collected dataset improved by 1.3%, 1.6%, and 0.6%, respectively. Through experiments on 21 leaf diseases (early blight, late blight, leaf blight, leaf spot, etc.) of five crops, namely, potato, maize, tomato, cashew, and cassava, our model achieved accurate and rapid detection of plant disease categories. This study provides a reference for research work and engineering applications in crop disease detection. Full article
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18 pages, 3568 KiB  
Article
Drought Stress Affects Spectral Separation of Maize Infested by Western Corn Rootworm
by Raquel Peron-Danaher, Lorenzo Cotrozzi, Ali Masjedi, Laramy S. Enders, Christian H. Krupke, Michael V. Mickelbart and John J. Couture
Agronomy 2023, 13(10), 2562; https://doi.org/10.3390/agronomy13102562 - 5 Oct 2023
Cited by 1 | Viewed by 997
Abstract
Root-feeding herbivores present challenges for insect scouting due to the reliance on aboveground visual cues. These challenges intensify in multi-stress environments, where one stressor can mask another. Pre-visual identification of plant stress offers promise in addressing this issue. Hyperspectral data have emerged as [...] Read more.
Root-feeding herbivores present challenges for insect scouting due to the reliance on aboveground visual cues. These challenges intensify in multi-stress environments, where one stressor can mask another. Pre-visual identification of plant stress offers promise in addressing this issue. Hyperspectral data have emerged as a measurement able to identify plant stress before visible symptoms appear. The effectiveness of spectral data to identify belowground stressors using aboveground vegetative measurements, however, remains poorly understood, particularly in multi-stress environments. We investigated the potential of hyperspectral data to detect Western corn rootworm (WCR; Diabrotica virgifera virgirefa) infestations in resistant and susceptible maize genotypes in the presence and absence of drought. Under well-watered conditions, the spectral profiles separated between WCR treatments, but the presence of drought eliminated spectral separation. The foliar spectral profiles separated under drought conditions, irrespective of WCR presence. Spectral data did not classify WCR well; drought was well classified, and the presence of drought further reduced WCR classification accuracy. We found that multiple plant traits were not affected by WCR but were negatively affected by drought. Our study highlights the possibility of detecting WCR and drought stress in maize using hyperspectral data but highlights limitations of the approach for assessing plant health in multi-stress conditions. Full article
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23 pages, 5810 KiB  
Article
A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s
by Jing Zhang, Jun Wang and Maocheng Zhao
Agronomy 2023, 13(7), 1779; https://doi.org/10.3390/agronomy13071779 - 30 Jun 2023
Cited by 4 | Viewed by 1744
Abstract
The real-time target detection of crop pests can help detect and control pests in time. In this study, we built a lightweight agricultural pest identification method based on modified Yolov5s and reconstructed the original backbone network in tandem with MobileNetV3 to considerably reduce [...] Read more.
The real-time target detection of crop pests can help detect and control pests in time. In this study, we built a lightweight agricultural pest identification method based on modified Yolov5s and reconstructed the original backbone network in tandem with MobileNetV3 to considerably reduce the number of parameters in the network model. At the same time, the ECA attention mechanism was introduced into the MobileNetV3 shallow network to meet the aim of effectively enhancing the network’s performance by introducing a limited number of parameters. A weighted bidirectional feature pyramid network (BiFPN) was utilized to replace the path aggregation network (PAnet) in the neck network to boost the feature extraction of tiny targets. The SIoU loss function was utilized to replace the CIoU loss function to increase the convergence speed and accuracy of the model prediction frame. The updated model was designated ECMB-Yolov5. In this study, we conducted experiments on eight types of common pest dataset photos, and comparative experiments were conducted using common target identification methods. The final model was implemented on an embedded device, the Jetson Nano, for real-time detection, which gave a reference for further application to UAV or unmanned cart real-time detection systems. The experimental results indicated that ECMB-Yolov5 decreased the number of parameters by 80.3% and mAP by 0.8% compared to the Yolov5s model. The real-time detection speed deployed on embedded devices reached 15.2 FPS, which was 5.7 FPS higher than the original model. mAP was improved by 7.1%, 7.3%, 9.9%, and 8.4% for ECMB-Yolov5 compared to Faster R-CNN, Yolov3, Yolov4, and Yolov4-tiny models, respectively. It was verified through experiments that the improved lightweight method in this study had a high detection accuracy while significantly reducing the number of parameters and accomplishing real-time detection. Full article
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