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AgriEngineering
Special Issues
Precision Agriculture Technologies for Management of Plant Diseases
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Precision Agriculture Technologies for Management of Plant Diseases

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

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 40196

Special Issue Editor

Dr. Redmond R. Shamshiri

E-Mail Website
Guest Editor
Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam, Germany
Interests: precision agriculture; wireless sensors; IoT; digital agriculture; system analysis and control; agricultural modeling and simulation; agricultural robotics; greenhouse automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Improving agricultural productivity requires innovative solutions that offer a better yield and quality for indoor and outdoor farming. Farmers require precision technology to obtain and interpret data to better control crop growth, preventing losses caused by adverse weather conditions or infectious pests and thus facilitating return on investments. Every year, plant diseases contribute to significant losses in global harvest, costing an estimated US$220 billion. Plant disease assessment is conducted to analyze the measurement of disease/pathogens (phythopathometry), which is fundamental for the estimation of disease intensity and crop loss. Abundant use of chemicals such as bactericides, fungicides, and nematicides to control plant diseases is causing adverse effects to many agroecosystems. An accurate and reliable approach is needed in plant disease assessment to increase plant disease identification and severity estimation. With the advances of electronic and information technologies, various sensing systems and algorithms have been developed for early detection of disease symptoms before they are visible to the naked eye. Precision plant protection offers a nondestructive means of managing plant diseases based on the concept of spatiotemporal variability. Currently, there are three main remote sensing platforms for this purpose, including close-range such as ground-based or handheld sensors, middle-range such as drone-based sensors or imaging devices mounted on autonomous unmanned aerial vehicles (UAVs), and far-range platforms such as piloted airplanes or satellite-based sensors. Different customized algorithms and techniques, including artificial intelligent and deep learning methods, have been proposed to process sensor data. Plant disease assessment aids researchers and farmers in evaluating the cause of disease and extent of damage (physical and economic).

This Special Issue is aimed at bringing together research reports that describe new perspectives recently developed in plant disease management, based on innovative tools emerging from basic and applied research. The objective of the Special Issue is to increase awareness of the implications of using precision agriculture solutions for managing plant disease problems in a balanced and optimized manner.

Contributions are expected to deal with (but not limited to) the use of precision agriculture tools, wireless sensors, reflectance spectroscopy, artificial intelligence, wavelet analysis, imaging and non-imaging spectroscopy, wavelet analysis, spectral disease index, and any of the areas described in the keywords that are related to assessment and monitoring of plant diseases.

Dr. Redmond R. Shamshiri
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

  • Precision agriculture
  • Digital agriculture
  • Spectral imaging
  • Infrared Spectroscopy
  • Remote sensing
  • Multispectral sensor
  • Hyperspectral sensor
  • Nondestructive sensing
  • Multispectral and hyperspectral sensors and vegetation indexes
  • Fluorescence and thermal imaging
  • Fluorescence and thermal imaging
  • Smart Sensors for determination of plant disease
  • Sensors solution for precision agriculture
  • Multi-sensor systems, sensor fusion, data fusion
  • intelligent sensor for early disease detection
  • Vegetation index, NDVI
  • Artificial intelligence
  • Deep learning
  • Wavelet analysis
  • principal component analysis
  • Monitoring of different growth stages of crops and phenotyping
  • Sensors for detection of fruits and quality determination
  • Spectral data analytics
  • Prescription map
  • Detection and identification of crops and weeds
  • UAV/Drone
  • Advanced characterization techniques
  • Big data
  • Management zone
  • Data management
  • In situ sensing

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Published Papers (4 papers)

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Research

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12 pages, 2235 KiB  
Article
Detection of Black Spot of Rose Based on Hyperspectral Imaging and Convolutional Neural Network
by Jingjing Ma, Lei Pang, Lei Yan and Jiang Xiao
AgriEngineering 2020, 2(4), 556-567; https://doi.org/10.3390/agriengineering2040037 - 17 Nov 2020
Cited by 2 | Viewed by 3735
Abstract
Black spot is one of the seriously damaging plant diseases in China, especially in rose production. Hyperspectral technology reflects both external features and internal structure information of measured samples, which can be used to identify the disease. In this research, both the spectral [...] Read more.
Black spot is one of the seriously damaging plant diseases in China, especially in rose production. Hyperspectral technology reflects both external features and internal structure information of measured samples, which can be used to identify the disease. In this research, both the spectral and image features of two infected roses with black spot were used to train a convolutional neural network (CNN) model. Multiple scattering correction (MSC) and standard normal variable (SNV) methods were applied to preprocess the spectral data. Cropping, median filtering and binarization were pretreatments used on the hyperspectral images. Three CNN models based on Alexnet, VGG16 and neural discriminative dimensionality reduction (NDDR) were evaluated by analyzing the classification accuracy and loss function. The results show that the CNN model based on the fusion of features has higher accuracy. The highest accuracies of detection of blackspot in different roses are 12–26 (100%) and 13–54 (99.95%), applying the NDDR-CNN model. Therefore, this research indicates that the spectral analysis based on CNN can detect black spot of roses, which provides a reference for the detection of other plant diseases, and has favorable research significance as well as prospect for development. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Management of Plant Diseases)
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18 pages, 16439 KiB  
Article
A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images
by Kavir Osorio, Andrés Puerto, Cesar Pedraza, David Jamaica and Leonardo Rodríguez
AgriEngineering 2020, 2(3), 471-488; https://doi.org/10.3390/agriengineering2030032 - 28 Aug 2020
Cited by 145 | Viewed by 14123
Abstract
Weed management is one of the most important aspects of crop productivity; knowing the amount and the locations of weeds has been a problem that experts have faced for several decades. This paper presents three methods for weed estimation based on deep learning [...] Read more.
Weed management is one of the most important aspects of crop productivity; knowing the amount and the locations of weeds has been a problem that experts have faced for several decades. This paper presents three methods for weed estimation based on deep learning image processing in lettuce crops, and we compared them to visual estimations by experts. One method is based on support vector machines (SVM) using histograms of oriented gradients (HOG) as feature descriptor. The second method was based in YOLOV3 (you only look once V3), taking advantage of its robust architecture for object detection, and the third one was based on Mask R-CNN (region based convolutional neural network) in order to get an instance segmentation for each individual. These methods were complemented with a NDVI index (normalized difference vegetation index) as a background subtractor for removing non photosynthetic objects. According to chosen metrics, the machine and deep learning methods had F1-scores of 88%, 94%, and 94% respectively, regarding to crop detection. Subsequently, detected crops were turned into a binary mask and mixed with the NDVI background subtractor in order to detect weed in an indirect way. Once the weed image was obtained, the coverage percentage of weed was calculated by classical image processing methods. Finally, these performances were compared with the estimations of a set from weed experts through a Bland–Altman plot, intraclass correlation coefficients (ICCs) and Dunn’s test to obtain statistical measurements between every estimation (machine-human); we found that these methods improve accuracy on weed coverage estimation and minimize subjectivity in human-estimated data. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Management of Plant Diseases)
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14 pages, 5184 KiB  
Article
Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing
by Xiaoling Deng, Zejing Tong, Yubin Lan and Zixiao Huang
AgriEngineering 2020, 2(2), 294-307; https://doi.org/10.3390/agriengineering2020019 - 21 May 2020
Cited by 93 | Viewed by 7211
Abstract
Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and [...] Read more.
Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Management of Plant Diseases)
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Review

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17 pages, 3339 KiB  
Review
Deep Learning Application in Plant Stress Imaging: A Review
by Zongmei Gao, Zhongwei Luo, Wen Zhang, Zhenzhen Lv and Yanlei Xu
AgriEngineering 2020, 2(3), 430-446; https://doi.org/10.3390/agriengineering2030029 - 14 Jul 2020
Cited by 84 | Viewed by 12832
Abstract
Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning [...] Read more.
Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning techniques trigger revolutions for precision agriculture based on deep learning and big data. In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis. We compiled the current sensor tools and deep learning principles involved in plant stress phenotyping. In addition, we reviewed a variety of deep learning applications/functions with plant stress imaging, including classification, object detection, and segmentation, of which are closely intertwined. Furthermore, we summarized and discussed the current challenges and future development avenues in plant phenotyping. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Management of Plant Diseases)
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