Journal
JOURNAL OF STORED PRODUCTS RESEARCH
Volume 88, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jspr.2020.101667
Keywords
Bioacoustic; Artificial neural network; Infestation; Bulk legume storage
Categories
Funding
- Indian Council of Agricultural Research-Central Institute of Agricultural Engineering, Bhopal (MP) India
- Total Project Solutions Pvt. Ltd.
Ask authors/readers for more resources
Acoustical detection of insects feeding and crawling sounds was used to automatically monitor internal and external grain feeding bruchids in order to assess the growth and density of food legume bruchids (Callosobruchus chinensis and Callosobruchus maculatus) in bulk stored chickpea and green gram. Bruchids hidden inside the grain kernels were detected acoustically through amplification and filtering of their mobility and feeding sounds. The multivariate technique of artificial neural network (ANN) was applied to assess and predict the bruchids' density in bulk stored legumes. Five levels of bruchids density (0, 5, 10 15 and 20 bruchids per 500 g) were monitored under without insulation and with insulated condition on the basis of formant parameter obtained by analysis of the acoustic sensor data. The K fold validation method with back propagation multilayer perceptron methodology was used for the prediction of bruchids densities. The maximum and minimum values of accuracy (R-2) of 0.99, 0.98 and 0.90, 0.89 could be achieved for both bruchids in stored green gram and chickpea under insulation and without insulation for the training and validation dataset, respectively. Least RMSE (0.82 and 0.89) was obtained for C. maculatus in sound insulated stored green gram for training and validation dataset, respectively. The accuracy of prediction and validation of experimental data with low RMSE and high R-2 values for both the food legumes indicated that the ANN modeling performed well in predicting bruchids density. Hence it can be concluded that, best prediction was obtained for the C. maculatus for green gram under insulated condition. The results further corroborated that bioacoustic detection technique with ANN provided a reliable and accurate monitoring technique for bruchids. The developed technique can be adopted in large bulk storage grain systems for the selected legumes for predicting and assessing the growth of bruchids thereby leading to safer storage. (C) 2020 Published by Elsevier Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available