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مقالات فارسیISIکنفرانسهاژورنالها

Classification using radial-basis neural networks based on thermographic assessment of Botrytis cinerea infected cut rose flowers treated with methyl jasmonate

محل انتشار: Journal of Crop Protection، دوره: 5، شماره: 4
سال انتشار: 1395
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 51

فایل این مقاله در 12 صفحه با فرمت PDF قابل دریافت می باشد

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لینک ثابت به این مقاله:
https://civilica.com/doc/1811447

شناسه ملی سند علمی:

JR_JCP-5-4_010

تاریخ نمایه سازی: 13 آبان 1402

چکیده مقاله:

Many environmental and physiological factors affect plant temperature. The objective of this study was to use thermal imagery to investigate robust features for early diagnosis of Botrytis cinerea infection in cut rose flowers under the postharvest application of Methyl Jasmonate (MeJA). Three cases treated with different concentrations of MeJA (۰.۱, ۰.۲, and ۰.۳ µl.l−۱), a control (۰ µl.l−۱ MeJA) and an ethanol-treated case (۲۰ µl.l−۱ ethanol) were considered as five treatments in this study. Infrared images of MeJA-treated and non-treated flowers were captured during five consecutive days. Eight days after inoculation, disease severity in all concentrations of MeJA was significantly lower than that of control and ethanol treatments. Maximum temperature difference (MTD) index and median temperature could be used to diagnose the existence and growth of fungal pathogen, at least a day before any significant visual symptoms appear. To identify some robust features for classifying the infected and non-infected flowers, analysis of temperature frequency distribution was implemented. Laplace and normal distributions were considered as the best fitted probability distributions based on the shape of thermal histograms. Parameters of normal and Laplace probability density functions were estimated and the most effective attributes were selected. A radial-basis-function neural network with ۶۰ neurons in the hidden layer was designed to classify and distinguish the infected flowers from the healthy ones. Results showed that the network can classify the infected and non-infected flowers with a ۹۶.۴% correct estimation rate.

کلیدواژه ها:

Laplace distribution ، Median temperature ، Normal distribution ، Thermal histogram

نویسندگان

Mehrnoosh Jafari

Department of Bio-System Mechanic Engineering, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

Saeid Minaei

Department of Bio-System Mechanic Engineering, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

Naser Safaie

Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

Farah Torkamani-Azar

Department of Electrical Engineering, College of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, Iran.

Mehdi Sadeghi

Department of Horticultural Sciences, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

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