scholarly journals Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Joe Johnson ◽  
Geetanjali Sharma ◽  
Srikant Srinivasan ◽  
Shyam Kumar Masakapalli ◽  
Sanjeev Sharma ◽  
...  
Keyword(s):  
Binary Classification ◽  
Color Space ◽  
Detection Performance ◽  
Automated System ◽  
Automated Identification ◽  
Potato Leaf ◽  
Potato Blight ◽  
Separate Model ◽  
Field Environment ◽  
Blight Disease

Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce. Manual detection of blight disease can be cumbersome and may require trained experts. To overcome these issues, we present an automated system using the Mask Region-based convolutional neural network (Mask R-CNN) architecture, with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions. The approach uses transfer learning, which can generate good results even with small datasets. The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day. The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf. The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches, which can confound the outcome of binary classification. To improve the detection performance, the original RGB dataset was then converted to HSL, HSV, LAB, XYZ, and YCrCb color spaces. A separate model was created for each color space and tested on 417 field-based test images. This yielded 81.4% mean average precision on the LAB model and 56.9% mean average recall on the HSL model, slightly outperforming the original RGB color space model. Manual analysis of the detection performance indicates an overall precision of 98% on leaf images in a field environment containing complex backgrounds.

scholarly journals The Effectiveness of Endophytic Fungi Origin of Potato Plant Roots in Coffee Skin Compost Media to Suppress Development of Potato Leaf Disease (Phytophtora infestans)

2020 ◽  
Vol 2 (1) ◽  
pp. 101-105
Author(s):  
Anna Fitriana ◽  
Lukman Hakim ◽  
Marlina Marlina
Keyword(s):  
Experimental Method ◽  
Endophytic Fungi ◽  
Potato Plant ◽  
Potato Production ◽  
Leaf Blight ◽  
Plant Roots ◽  
Potato Leaf ◽  
Potato Plants ◽  
Pathogen Attack ◽  
Blight Disease

Potato leaf blight is caused by Phytophthora infestans fungus is one of the important diseases in potato plants. The decrease in potato production due to P. infestans can reach 90%. Until now, P. infestans pathogen attack is an important problem and there is no fungicide that is really effective against the disease. This study aims to examine the effectiveness of endophytic fungi from potato roots in suppressing the development of P. infestans potato leaf blight disease carried out at University Farm Stasiun Riset Bener Meriah (UFBM) Syiah Kuala University Tunyang Village, Timang Gajah District, Bener Meriah Regency from May to October 2014. The method used is the experimental method. The results of this study indicate that endophytic fungi from the roots of potato plants in coffee skin compost media can suppress the development of leaf blight caused by P. infestans, endophytic fungi from potato plant roots in coffee skin compost media. The best results were found in B9 endophytic fungi isolates with the intensity of the pathogen attack P. infestans 48.00%, the intensity of damage to potato plants due to pathogen P. infestans and 2.60%, the weight of healthy tubers 332.4 grams.

scholarly journals Functional features of the automated accounting and control system of signalling, centralization and blocking devices

2021 ◽  
Vol 264 ◽  
pp. 05052
Author(s):  
Dilshod Baratov
Keyword(s):  
Control System ◽  
Automated System ◽  
Data Input ◽  
Automated Identification ◽  
Automated Method ◽  
Coding Method ◽  
Functional Features ◽  
System Requirements ◽  
And Control ◽  
New Devices

The article illustrates the functional features of the automated system of operation of signalling, centralization and blocking devices, shows the interface of the automated system of accounting and control of railway automation and telemechanics devices, presents the system requirements and testing of the automated method. The process manages, and metering devices of automatics and telemechanics in the use of QR-coding method should be used for automated identification of devices of signalling, centralization and blocking with the aim of collecting data about installed devices, checking the correct replacement devices, data input on the implementation of the repair and acceptance, automated input of data on new devices submitted for repair and process areas.

scholarly journals The Impact of Color Space and Intensity Normalization to Face Detection Performance

2017 ◽  
Vol 15 (4) ◽  
pp. 1895
Author(s):  
I Nyoman Gede Arya Astawa ◽  
I Ketut Gede Darma Putra ◽  
I Made Sudarma ◽  
Rukmi Sari Hartati
Keyword(s):  
Face Detection ◽  
Color Space ◽  
Detection Performance ◽  
The Impact ◽  
Intensity Normalization

scholarly journals Enhancement Image Intensity of HSV Color Space

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2583-2585
Keyword(s):  
Light Intensity ◽  
Face Detection ◽  
Color Image ◽  
Color Space ◽  
Detection Performance ◽  
Lighting Condition ◽  
Hsv Color Space ◽  
Number Of Faces ◽  
Intensity Normalization ◽  
Poor Lighting

One of factor that affects technology in face detecting or recognizing is illumination. Poor lighting can cause difficulty to the system to recognize face. Although it is over exposure or under exposure. By doing color image processing, it supports the system to detect face in a poor lighting condition. This research used lighting intensity normalization method to increase face detection performance. This method can normalize the light intensity especially on the face lighting. We normalize the light intensity through HSV color space. HSV color space has saturation which is amount of light in the image. The method proceed saturation in image to increase face detection performance. Total number of faces we had tested is 286 faces, the system detect 243 faces before intensity normalization proceed. Whereas, after normalization process, it detects more faces which is 279 faces. As we can see, the percentage improvement before to after intensity normalization is 84.97% to 97.55%. This is 12.58% improvement. We can say this method helps face detection to increase it performance.

scholarly journals Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 914
Author(s):  
Sheng Feng ◽  
Xiaoqiang Hua ◽  
Xiaoqian Zhu
Keyword(s):  
Dimensionality Reduction ◽  
Signal Detection ◽  
Binary Classification ◽  
Real Life ◽  
Classification Problem ◽  
Information Geometry ◽  
Detection Performance ◽  
Complex Signal ◽  
Low Snr ◽  
Short Time

In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract abundant features, then exploit the advantages of matrix information geometry technique by constructing the high-dimensional features as symmetric positive definite (SPD) matrices. In this case, our task for signal detection becomes a binary classification problem lying on an SPD manifold. Promoting the discrimination of heterogeneous samples through information geometric DR technique that is dedicated to SPD manifold, our proposed detector achieves satisfactory signal detection performance in low SNR cases using the K distribution simulation and the real-life sea clutter data, which can be widely used in the field of signal detection.

scholarly journals Automated identification of insect vectors of Chagas disease in Brazil and Mexico: the Virtual Vector Lab

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3040 ◽  
Author(s):  
Rodrigo Gurgel-Gonçalves ◽  
Ed Komp ◽  
Lindsay P. Campbell ◽  
Ali Khalighifar ◽  
Jarrett Mellenbruch ◽  
...  
Keyword(s):  
Trypanosoma Cruzi ◽  
Chagas Disease ◽  
Disease Transmission ◽  
Automated System ◽  
Correct Identification ◽  
Insect Vectors ◽  
Automated Identification ◽  
Considerable Potential ◽  
Triatomine Bugs ◽  
Fully Automated System

Identification of arthropods important in disease transmission is a crucial, yet difficult, task that can demand considerable training and experience. An important case in point is that of the 150+ species of Triatominae, vectors ofTrypanosoma cruzi, causative agent of Chagas disease across the Americas. We present a fully automated system that is able to identify triatomine bugs from Mexico and Brazil with an accuracy consistently above 80%, and with considerable potential for further improvement. The system processes digital photographs from a photo apparatus into landmarks, and uses ratios of measurements among those landmarks, as well as (in a preliminary exploration) two measurements that approximate aspects of coloration, as the basis for classification. This project has thus produced a working prototype that achieves reasonably robust correct identification rates, although many more developments can and will be added, and—more broadly—the project illustrates the value of multidisciplinary collaborations in resolving difficult and complex challenges.

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