Enhancing Insect Species Identification in Agriculture using Fusion BiLSTM Network and VGG-16 CNN Architecture

In agricultural sectors like wheat, paddy, flowers, crops and maize production, insect infestations lead to significant financial losses. Swift identification of the insect species is vital to mitigate economic impacts. However, farmers often struggle to distinguish between various agricultural pests due to their limited experience and knowledge. Tackling this challenge involves harnessing advanced technologies such as the VGG-16 convolutional neural network (CNN) architecture, alongside other computational methods. VGG-16, renowned for its effectiveness in image classification tasks, can be employed to automatically extract features from the data. In our study, we propose integrating VGG-16 as part of a fusion BiLSTM network, which incorporates bidirectional long short-term memory (BiLSTM) layers, considering temporal information. This approach offers a promising solution to insect species identification in agricultural settings.