REVIEW OF PLANT DISEASE DETECTION METHODS FROM PHOTOGRAPHY

2020 ◽  
Author(s):  
D. O. Logashov ◽  
◽  
A. I. Veselov ◽  
Keyword(s):  
Plant Disease ◽  
Detection Methods ◽  
Disease Detection

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

Behavior assessment and applications for BRD diagnosis: preweaned dairy calves

2020 ◽  
pp. 1-4
Author(s):  
Catie Cramer ◽  
Theresa L. Ollivett
Keyword(s):  
Clinical Signs ◽  
Sickness Behavior ◽  
Behavioral Changes ◽  
Dairy Calves ◽  
Detection Methods ◽  
Disease Detection ◽  
Test Characteristics ◽  
Behavioral Studies ◽  
Detection Program ◽  
Behavioral Assessments

Abstract Bovine respiratory disease (BRD) is an important disease in dairy calves due to its long-lasting effects. Early identification results in better outcomes for the animal, but producers struggle to identify all calves with BRD. Sickness behavior, or the behavioral changes that accompany illness, has been investigated for its usefulness as a disease detection tool. Behavioral changes associated with BRD include decreased milk intake and drinking speed, depressed attitude, and less likelihood of approaching a novel object or stationary human. Behavioral measurements are useful, as they can be collected automatically or with little financial input. However, one limitation of many BRD behavioral studies includes the use of either lung auscultation or clinical signs as reference methods, which are imperfect. Additionally, external factors may influence the expression of sickness behavior, which can affect if and when behavior can be used to identify calves with BRD. Behavioral measures available to detect BRD lack adequate sensitivity and specificity to be the sole means of disease detection, especially when detection tools, such as calf lung ultrasound, have better test characteristics. However, using behavioral assessments in addition to other detection methods can allow for a robust BRD detection program that can ameliorate the consequences of BRD.

scholarly journals Spread and Increase of Ratoon Stunting Disease of Sugarcane and Comparison of Disease Detection Methods

Plant Disease ◽  
1999 ◽  
Vol 83 (12) ◽  
pp. 1170-1175 ◽  
Author(s):  
J. W. Hoy ◽  
M. P. Grisham ◽  
K. E. Damann
Keyword(s):  
Yield Loss ◽  
Detection Method ◽  
Xylem Sap ◽  
Disease Spread ◽  
Detection Methods ◽  
Disease Detection ◽  
Correct Identification ◽  
Dot Blot ◽  
Ratoon Stunting Disease ◽  
Enzyme Immunoassays

The spread and increase of ratoon stunting disease (RSD) resulting from two mechanical harvests were compared in eight sugarcane cultivars at two locations. RSD spread and increase were detected in the ratoon crops grown after each harvest and varied among cultivars and locations. Disease spread and increase were greater in plants grown from stalks collected at the first harvest than in the first ratoon growth from the harvested field. RSD infection was determined using five disease detection methods: alkaline-induced metaxylem autofluorescence; microscopic examination of xylem sap; and dot blot, evaporative-binding, and tissue blot enzyme immunoassays. The tissue blot enzyme immunoassay was the most accurate RSD detection method. The dot blot and evaporative-binding enzyme immunoassays were the least sensitive for detection of RSD-infected stalks, and alkaline-induced metaxylem autofluorescence was least accurate for correct identification of noninfected stalks. The results indicate that disease spread and increase are variable even among cultivars susceptible to yield loss due to RSD, and the greatest threat of disease spread and increase occurs at planting.

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