Developing an image processing based algorithm to detect and count soybean aphids under field conditions

2016 ◽  
Keyword(s):  
Image Processing ◽  
Field Conditions ◽  
Soybean Aphids

scholarly journals Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Léa Tresch ◽  
Yue Mu ◽  
Atsushi Itoh ◽  
Akito Kaga ◽  
Kazunori Taguchi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Field Conditions ◽  
Processing Step ◽  
Field Phenotyping ◽  
Binary Segmentation ◽  
Aerial Vehicle ◽  
Uav Images

Microplot extraction (PE) is a necessary image processing step in unmanned aerial vehicle- (UAV-) based research on breeding fields. At present, it is manually using ArcGIS, QGIS, or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semiautomatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1) binary segmentation, (2) microplot extraction, (3) production of ∗.shp files to enable further file manipulation, and (4) projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of the proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: the average IOU (±SD) of all trials was 91% (±3).

scholarly journals Detection of the Progression of Anthesis in Field-Grown Maize Tassels: A Case Study

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Seyed Vahid Mirnezami ◽  
Srikant Srinivasan ◽  
Yan Zhou ◽  
Patrick S. Schnable ◽  
Baskar Ganapathysubramanian
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Morphological Diversity ◽  
Semantic Segmentation ◽  
Time Lapse ◽  
Field Conditions ◽  
State University ◽  
Subsequent Capture ◽  
Segmentation Image ◽  
Pollen Shed

The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk (stigma) and fertilization of the ovules. Both the amount and timing of pollen shed are physiological traits that impact the production of a hybrid seed. This study describes an automated end-to-end pipeline that combines deep learning and image processing approaches to extract tassel flowering patterns from time-lapse camera images of plants grown under field conditions. Inbred lines from the SAM and NAM diversity panels were grown at the Curtiss farm at Iowa State University, Ames, IA, during the summer of 2016. Using a set of around 500 pole-mounted cameras installed in the field, images of plants were captured every 10 minutes of daylight hours over a three-week period. Extracting data from imaging performed under field conditions is challenging due to variabilities in weather, illumination, and the morphological diversity of tassels. To address these issues, deep learning algorithms were used for tassel detection, classification, and segmentation. Image processing approaches were then used to crop the main spike of the tassel to track reproductive development. The results demonstrated that deep learning with well-labeled data is a powerful tool for detecting, classifying, and segmenting tassels. Our sequential workflow exhibited the following metrics: mAP for tassel detection was 0.91, F1 score obtained for tassel classification was 0.93, and accuracy of semantic segmentation in creating a binary image from the RGB tassel images was 0.95. This workflow was used to determine spatiotemporal variations in the thickness of the main spike—which serves as a proxy for anthesis progression.

Detection of soybean aphids in a greenhouse using an image processing technique

2017 ◽  
Vol 132 ◽  
pp. 63-70 ◽  
Author(s):  
Mohammadmehdi Maharlooei ◽  
S. Sivarajan ◽  
Sreekala G. Bajwa ◽  
Jason P. Harmon ◽  
John Nowatzki
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Soybean Aphids

Identification and Counting of Soybean Aphids from Digital Images Using Shape Classification

2017 ◽  
Vol 60 (5) ◽  
pp. 1467-1477 ◽  
Author(s):  
Sunoj Shajahan ◽  
Saravanan Sivarajan ◽  
Mohammadmehdi Maharlooei ◽  
Sreekala G. Bajwa ◽  
Jason P. Harmon ◽  
...  
Keyword(s):  
Image Processing ◽  
Shape Analysis ◽  
Processing Method ◽  
Shape Parameters ◽  
Visual Fatigue ◽  
Soybean Aphids ◽  
Image Processing Method ◽  
Lighting Conditions ◽  
Automatic Image Processing ◽  
Manual Counting

Abstract. Soybean aphids are serious pests, causing negative yield impacts in the crop. Assessing their population is essential for making appropriate pesticide application decisions. Manual identification and counting, which is commonly performed to determine the economic threshold level, is time-consuming, laborious, and causes visual fatigue. In this study, an automatic image processing method was developed to identify and count aphids as well as exoskeletons and leaf spots on soybean leaves based on shape analysis. Aphid-infested soybean trifoliates were obtained at three infestation rates (low, medium, and high). Images of the front sides of the leaves were captured in the laboratory with three cameras (digital single-lens reflex or DSLR, consumer-grade digital, and smartphone) under two lighting conditions (direct and indirect). The shape parameters considered were area, perimeter, convex area, eccentricity, aspect ratio, solidity, hollowness, and roundness. Among the shape parameters tested, hollowness was the best in identifying aphids and was therefore used for developing the object classification algorithm. Of the three cameras tested, images from the consumer-grade digital camera produced the best identification accuracy (>82.4%), followed by the DSLR camera (>81.2%) and smartphone camera (>37.9%). Statistical analysis revealed that the accuracies did not differ significantly under different lighting conditions (p = 0.43), but the accuracies differed for the smartphone camera compared to the DSLR and consumer-grade digital cameras (p = 8.87 × 10-10). The results of automatic and manual counting were very well correlated (r = 0.92). The automatic image processing method achieved more rapid counting (<2 s per image after loading the image) compared to manual counting (~5 min per image). The developed approach for aphid identification and counting can be easily applied to other pest identification issues with minor modifications to the algorithm. Keywords: Aphids, Classification, Image processing, Segmentation, Shape analysis, Soybean.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

The 'well-known' theory of electron image formation

1983 ◽  
Vol 41 ◽  
pp. 288-289
Author(s):  
M.A. O'Keefe ◽  
W.O. Saxton
Keyword(s):  
Image Processing ◽  
Image Formation ◽  
Source Profiles ◽  
Electron Image

A recent paper by Kirkland on nonlinear electron image processing, referring to a relatively new textbook, highlights the persistence in the literature of calculations based on incomplete and/or incorrect models of electron imageing, notwithstanding the various papers which have recently pointed out the correct forms of the appropriate equations. Since at least part of the problem can be traced to underlying assumptions about the illumination coherence conditions, we attempt to clarify both the assumptions and the corresponding equations in this paper, illustrating the effects of an incorrect theory by means of images calculated in different ways.The first point to be made clear concerning the illumination coherence conditions is that (except for very thin specimens) it is insufficient simply to know the source profiles present, i.e. the ranges of different directions and energies (focus levels) present in the source; we must also know in general whether the various illumination components are coherent or incoherent with respect to one another.

Recent Applications of High Resolution Electron Microscopy to Crystalline Arrays of Virus Particles

1978 ◽  
Vol 36 (3) ◽  
pp. 470-482 ◽  
Author(s):  
R.W. Horne
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Analytical Techniques ◽  
Staining Technique ◽  
High Resolution Electron Microscopy ◽  
Optical Diffraction ◽  
Full Potential ◽  
Virus Particles ◽  
Resolution Electron ◽  
Wide Range

The technique of surrounding virus particles with a neutralised electron dense stain was described at the Fourth International Congress on Electron Microscopy, Berlin 1958 (see Home & Brenner, 1960, p. 625). For many years the negative staining technique in one form or another, has been applied to a wide range of biological materials. However, the full potential of the method has only recently been explored following the development and applications of optical diffraction and computer image analytical techniques to electron micrographs (cf. De Hosier & Klug, 1968; Markham 1968; Crowther et al., 1970; Home & Markham, 1973; Klug & Berger, 1974; Crowther & Klug, 1975). These image processing procedures have allowed a more precise and quantitative approach to be made concerning the interpretation, measurement and reconstruction of repeating features in certain biological systems.

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