Corrigendum to ‘Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection’ [Comput. Electron. Agric. 158 (2019) 219–225]

2019 ◽  
Vol 160 ◽  
pp. 188
Author(s):  
Md. Sultan Mahmud ◽  
Young Ki Chang ◽  
Qamar U. Zaman ◽  
Travis J. Esau ◽  
Gordon W. Price ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Machine Vision ◽  
Disease Detection ◽  
Lighting Condition ◽  
Comput Electron ◽  
Powdery Mildew Disease

Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection

2019 ◽  
Vol 158 ◽  
pp. 219-225 ◽  
Author(s):  
Md. Sultan Mahmud ◽  
Qamar U. Zaman ◽  
Travis J. Esau ◽  
Gordon W. Price ◽  
Balakrishnan Prithiviraj
Keyword(s):  
Powdery Mildew ◽  
Machine Vision ◽  
Disease Detection ◽  
Lighting Condition ◽  
Powdery Mildew Disease

scholarly journals Detection of Powdery Mildew Disease of Beans in India : A Review

2016 ◽  
Vol 9 (3) ◽  
pp. 226-234
Author(s):  
Kuldeep Singh ◽  
Satish Kumar ◽  
Pawan Kaur
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Powdery Mildew ◽  
Management Strategies ◽  
Cost Effective ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Spectroscopic Techniques ◽  
Powdery Mildew Disease

Powdery mildew disease of beans in India causes major economic losses in agriculture. For sustainable agriculture detection and identification of diseases in plants is very important. In this review, we are trying to identify the powdery mildew disease of beans crop by using some image processing and pattern recognition techniques and comparing with molecular and spectroscopic techniques. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. The present review recognizes the need for developing a rapid, cost-effective, and reliable health monitoring techniques that would facilitate advancements in agriculture. These technologies include image processing and pattern recognition based plant disease detection methods

A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves

2021 ◽  
Vol 183 ◽  
pp. 106042
Author(s):  
Jaemyung Shin ◽  
Young K. Chang ◽  
Brandon Heung ◽  
Tri Nguyen-Quang ◽  
Gordon W. Price ◽  
...  
Keyword(s):  
Deep Learning ◽  
Powdery Mildew ◽  
Disease Detection ◽  
Learning Approach ◽  
Powdery Mildew Disease ◽  
Rgb Image

scholarly journals Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1027
Author(s):  
Md Sultan Mahmud ◽  
Qamar U. Zaman ◽  
Travis J. Esau ◽  
Young K. Chang ◽  
G. W. Price ◽  
...  
Keyword(s):  
Image Processing ◽  
Powdery Mildew ◽  
Machine Vision ◽  
Detection System ◽  
Vision System ◽  
Machine Vision System ◽  
Field Site ◽  
System A ◽  
Processing Program ◽  
Powdery Mildew Disease

Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (>8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.

Incidence of powdery mildew on cucurbit plants in Katsina, Nigeria

2019 ◽  
pp. 05-09
Keyword(s):  
Powdery Mildew ◽  
Low Cost ◽  
Frequency Of Occurrence ◽  
Sphaerotheca Fuliginea ◽  
Causal Organism ◽  
Powdery Mildews ◽  
Powdery Mildew Disease ◽  
Important Disease

The presence study deals with powdery mildews in various cucurbits in Katsina city (Barhim Estate, Kofar Durbi, Kofar Sauri, Kofar Marusa and Low Cost), Nigeria. The finding shows that the areas infested with powdery mildew is one of the important disease of cucurbits. The Sphaerotheca fuliginea was identified to be the causal organism present on all observed cucurbits in the study. Highest frequency of disease was found in Kofar Sauri(79%) fallowed by Kofar Marusa (68%), Kofar Durbi (66%), Barhim Estate (65%) and the lowest frequency of occurrence of disease was found in Low Cost (55%).The intensity of the disease was moderate to severe in general but it was high in many fields, the area-wise variation was also noticed. On vegetables, the highest frequency of occurrence of powdery mildew disease was observed on L. cylindrica (76.4%) followed by C. moschata (60%), C. sativus (59.3%), C. vulgaris (53.9%) and lowest was found on C. melo (44.4%). The highest intensity of disease was found on C. moschata, followed by L. cylindrica, C. sativus, C. vulgaris and C. melo.

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