Research on Plant Species Identification Based on Improved Convolutional Neural Network

Abstract

Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.

An Improved Res2Net-Based Model for Classifying the Appearance of Deer Antler Slices

“…In recent years, deep learning has been widely used in computer vision, and Convolutional Neural Networks (CNNs) have made significant contributions to image classification [5][6][7][8] . For instance, Lee et al [9] proposed a method to classify 32 plant images based on leaves' main vein and frequency domain data, as well as the distance between leaf contour and center of mass using Fast Fourier Transform achieving an average recognition accuracy of 97.19%.…”