An intelligent fruits classification in precision agriculture using bilinear pooling convolutional neural networks

With an increase in the consumption of fruits day by day, the yielding and production around the world are also increasing at a steady rate. Meanwhile, the workforce in the field becomes more challenging, there arises a need for automated solutions to maintain consistent output and quality of the product. An accurate, competent and consistent approach to classifying fruits and other agricultural products in precision agriculture is the foundation for a machine vision system to be successful and cost-effective. In this research work, Convolutional Neural Network (CNN)-based intelligent fruits classification utilizing the bilinear pooling with heterogeneous streams is proposed. The fruits classification problem is viewed as a fine-grained visual classification (FGVC) and the heterogeneous bilinear network is developed and compared with the normal implementations. The proposed CNN network is initialized with ImageNet weights and the pre-trained networks are used as components in the Bilinear Pooling CNN (BP-CNN). The CNNs used in the bilinear network function as feature extractors are then combined using the bilinear pooling function. The proposed BP-CNN-based intelligent classifier is trained and tested with Fruits-360, Imagenet and VegFru which are used by many researchers recently. The performance of the proposed BP-CNN model is validated using various metrics and compared with other existing CNN models. It is found that it outperforms all other methods with a classification accuracy of 99.69% and an F1 score of 0.9968.