Detection of pest species with different ratios in tea plant based on electronic nose. (24th January 2019)

• Record Type: Journal Article
• Authors: Sun, Yubing; Wang, Jun; Cheng, Shaoming; Wang, Yongwei
• Is Part Of: Annals of applied biology. Volume 174:Number 2(2019)
• Page Start: 209
• Page End: 218
• Publication Date: 2019-01-24
• Subjects: electronic nose -- pests -- ratio prediction -- tea plant; Crop science -- Periodicals; Plants, Protection of -- Periodicals; Crops -- Ecology -- Periodicals; 630
• DOI: 10.1111/aab.12485
• Languages: English
• ISSNs: 0003-4746

Abstract

Abstract : It is highly possible that tea ( Camellia sinensis ) plant is attacked by more than one pest species at the same time, and the determination of their proportion is of great significance to the management of tea plants. However, there are no literatures focusing on it previously. In this work, two pest species ( Ectropis obliqua and Ectropis grisescens ) in six different ratios (10:0, 8:2, 6:4, 4:6, 2:8 and 0:10) were applied to attack tea plants and electronic nose (E‐nose) was employed to detect them, labelled as group 10:0, 8:2, 6:4, 4:6, 2:8 and 0:10, respectively. Two prediction methods were applied to predict the ratio of E. obliqua and E. grisescens attacking tea plant and their performances were compared. The first method employed regression algorithm for prediction analysis based on the whole E‐nose data directly. The second method classified tea plants into three main classes (the first class contained group 10:0, the second class contained groups 8:2, 6:4, 4:6 and 2:8, and the third class contained group 0:10) first, then regression algorithm was applied to deal with the second class for prediction analysis. The results showed that the second method had a better performance. Its discrimination results showed 100% of the correct classification rate for training set and 93.75% for testing set. Meanwhile, its prediction results showed 0.0005 of root mean square error (RMSE) for calibration set, 0.0064 for validation set and 99.07% of fitting correlation coefficients ( R 2 ) for calibration set, 91.22% for validation set, which were acceptable for prediction analysis and proved that E‐nose was a feasible technique for pests' ratio prediction. Abstract : Detection of pest species with different ratios in tea plant through VOCs