Early Detection of Ripeness for the Picking of Xanthoceras Sorbifolium Using Feature Excitation-Based Broad Learning System

The seed oil of Xanthoceras sorbifolium is a new kind of vegetable oil which is beneficial to the body. However, during the ripening process, if not picked properly, resulting in seed waste and economic loss. Therefore, selecting the right picking time is of great significance for improving seed yield, reducing waste of labor and capital costs, and improving economic benefits. It is very challenging to achieve real-time and accurate classification of the images in the ripening stage of Xanthoceras sorbifolium with similar color, different shapes and serious background interference. So as to extract effective fruit features and improve the classification efficiency, a broad learning image classification method based on feature excitation (BL-SENet) was proposed in this paper. Firstly, a broad learning system (BLS) was constructed to extract the fruit features based on node activation function for the input layer. Secondly, feature excitation is carried out based on SENet, and the learning weights of features extracted based on broad learning mechanism are re-calibrated to improve the accuracy of network classification. Finally, based on the feature calibration, image classification is carried out by taking full advantage of the fast broad learning system. It is tested through experiments, the training accuracy of the proposed method is 100%, and the test accuracy is more than 80%, and it is the fastest among the comparison methods (except BLS). In order to promote the development of intelligent agriculture and realize intelligent mechanical picking, it provides effective visual information.