An Adaptive Features Fusion Convolutional Neural Network for Multi-Class Agriculture Pest Detection

Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color Histogram) and modified deep-learning architectures. We propose two models for this purpose: PestNet-EF and PestNet-LF. PestNet-EF uses an early fusion technique to integrate handcrafted and deep learning features, followed by adaptive feature selection methods such as CFS and Recursive Feature Elimination (RFE). PestNet-LF utilizes a late fusion technique, incorporating three additional layers (fully connected, softmax, and classification) to enhance performance. These models were evaluated across 15 classes of pests, including five classes each for rice, corn, and wheat. The performance of our suggested algorithms was tested against the IP102 dataset. Simulation demonstrates that the Pestnet-EF model achieved an accuracy of 96%, and the PestNet-LF model with majority voting achieved the highest accuracy of 94%, while PestNet-LF with the average model attained an accuracy of 92%. Also, the proposed approach was compared with existing methods that rely on hand-crafted and transfer learning techniques, showcasing the effectiveness of our approach in real-time pest detection for improved agricultural yield.