Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Abstract

Bamboos, also known as non-timber forest products (NTFPs) and belonging to the family Poaceae and subfamily Bambusoideae, have a wide range of flowering cycles from 3 to 120 years; hence, it is difficult to identify species. Here, the focus is on supervised machine learning (ML) and deep learning (DL) as a potential automated approach for the identification and classification of commercial bamboo species, with the help of the majority multiclass voting (MajMulVot) algorithm. We created an image dataset of 2000 bamboo instances, followed by a texture dataset prepared using local binary patterns (LBP) and gray-level cooccurrence matrix (GLCM)-based methods. First, we deployed five ML models for the texture datasets, where support vector machine (SVM) shows an accuracy rate of 82.27%. We next deployed five DL-based convolutional neural network (CNN) models for bamboo classification, namely AlexNet, VGG16, ResNet18, VGG19, and GoogleNet, using the transfer learning (TL) approach, where VGG16 prevails, with an accuracy rate of 88.75%. Further, a MajMulVot-based ensemble approach was introduced to improve the classification accuracy of all ML- and DL-based models. The ML-MajMulVot enhanced the accuracy for the texture dataset to 86.96%. In the same way, DL-MajMulVot increased the accuracy to 92.8%. We performed a comparative analysis of all classification models with and without K-fold cross-validation and MajMulVot methods. The proposed findings indicate that even difficult-to-identify species may be identified accurately with adequate image datasets. The suggested technology can also be incorporated into a mobile app to offer farmers effective agricultural methods.

Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network

“…Through multiple convolutional and pooling operations, and by employing the ReLU activation function after each convolution operation to strengthen the inter-layer connections, the image features are extracted, ultimately resulting in a feature matrix with a size of 4 × 4 × 512. Stacks of smaller convolutional kernels are a key element of VGG16 [34]. To address the issue of diminished model accuracy due to the small feature maps resulting from multiple convolution operations, an enhancement is made to the VGG16 network.…”