Assessment of bruchids density through bioacoustic detection and artificial neural network (ANN) in bulk stored chickpea and green gram. (September 2020)

• Record Type: Journal Article
• Authors: Banga, Km. Sheetal; Kotwaliwale, Nachiket; Mohapatra, Debabandya; Babu, V. Bhushana; Giri, Saroj Kumar; Bargale, Praveen Chandra
• Is Part Of: Journal of stored products research. Volume 88(2020)
• Publication Date: 2020-09
• Subjects: Bioacoustic -- Artificial neural network -- Infestation -- Bulk legume storage; Food -- Storage -- Periodicals; Farm produce -- Storage -- Diseases and injuries -- Periodicals; Entomology -- Periodicals; Food Contamination -- Periodicals; Food Preservation -- Periodicals; Insect Control -- Periodicals; Aliments -- Entreposage -- Périodiques; Produits agricoles -- Entreposage -- Maladies et dommages -- Périodiques; Electronic journals; 631.568
• DOI: 10.1016/j.jspr.2020.101667
• Languages: English
• ISSNs: 0022-474X

Abstract

Abstract: 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. Highlights: Prediction of bruchids density at an early stage of infestation is necessary. Artificial neural network modelling is useful for the prediction of bruchids infestation. Bioacoustic technology provides a non-destructive infestation detection method.