Effective Deep Learning Framework for Crop Pest Classification

Pest infestations in agricultural ecosystems present considerable risks to vital crops including potatoes, rice, wheat, maize, soybeans, sugarcane and chickpeas, all of which result in considerable economic detriment. Determining the precise insect species accountable for the infestation is critical in order to efficiently mitigate these losses. Notwithstanding this, farmers frequently encounter difficulties in precisely differentiating among different species of crop insects on account of their restricted expertise and experience in the field of entomology. Advanced computer-based technologies, including as convolutional neural networks (CNNs), provide promising solutions to this critical problem. These technologies are becoming increasingly popular. CNNs are well-known for their capacity to automatically extract and learn detailed features from image data. As a result, they are ideally suited for jobs that include picture categorization. Through the utilisation of CNNs, researchers have successfully classified a large number of photos spanning a variety of fields. In our study, we offer a unique strategy for improving insect categorization reliability in agricultural contexts. Specifically, we present a hybrid BiLSTM (Bidirectional Long Short-Term Memory) network structure designed for this application. This design combines the features of a model that has been trained and a BiLSTM layer to effectively collect temporal data required for insect categorization. Our proposed model seeks to address the issues of insect identifying species in agricultural environments through integrating the strengths of CNNs with the temporal retention of memory capabilities of BiLSTM networks. Through the utilization of this hybrid architecture, we anticipate achieving superior performance in insect classification tasks compared to traditional CNN-based approaches. By leveraging temporal features alongside learned image characteristics, our model endeavors to provide farmers and agricultural stakeholders with a robust tool for accurate and timely identification of crop pests, thereby enabling proactive pest management strategies and minimizing economic losses in agricultural production.