Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification

Abstract

The main purpose of this research is to apply image processing for plant identification in agriculture. This application field has so far received less attention rather than the other image processing applications domains. This is called the plant identification system. In the plant identification system, the conventional technique is dealt with looking at the leaves and fruits of the plants. However, it does not take into account as a cost effective approach because of its time consumption. The image processing technique can lead to identify the specimens more quickly and classify them through a visual machine method. This paper proposes a methodology for identifying the plant leaf images through several items including GIST and Local Binary Pattern (LBP) features, three kinds of geometric features, as well as color moments, vein features, and texture features based on lacunarity. After completion of the processing phase, the features are normalized, and then Pbest-guide binary particle swarm optimization (PBPSO) is developed as a novel method for reduction of the features. In the next phase, these features are employed for classification of the plant species. Different machine learning classifiers are evaluated including k-nearest neighbor, decision tree, naï ve Bayes, and multi-SVM. We tested our proposed technique on Flavia and Folio leaf datasets. The final results demonstrated that the decision tree has the best performance. The results of the experiments reveal that the proposed algorithm shows the accuracy of 98.58% and 90.02% for the "Flavia" and "Folio" datasets, respectively.

Plant-CNN-ViT: Plant Classification with Ensemble of Convolutional Neural Networks and Vision Transformer

“…HOG with SVM [1] 97.00 C-SIFT with KD tree [1] 98.00 MSER with KD tree [1] 90.00 KNN [2] 98.93 ROM-LBP [3] 98.94 Cosine KNN [4] 95.50 SVM [4] 89.90 Patternnet Neural Network [4] 72.20 PBPSO with Decision Tree [6] 98.58 PBPSO with SVM [6] 96.12 PBPSO with Naive Bayes [6] 92.01 PBPSO with KNN [6] 94.89 Random Forest [7] 84.11 SVM [7] 79.05 Logistic Regression [7] 84.11 KNN [7] 80.10 Naive Bayes [7] 72.25 CNN [9] 87.92 D-Leaf with ANN [10] 94.63 VGG16 and LDA [12] 99.10 VGG16 [12] 99.11 CNN-RNN [12] 99.11 VGG19 with Logistic Regression [13] 96.25 SWP-LeafNet [17] 99.67 Table 3 presents a comparison of different methods for plant leaf classification on the Folio Leaf dataset, which is characterized by an average of 14 training samples per class. Consequently, the achieved accuracy of all methods was relatively lower.…”

Application of Data Visualization and Big Data Analysis in Intelligent Agriculture

“…Formula (22) shows that the value of VP t can be obtained by weighted average operation on b t , b t -1 , ..., b ti , using the coefficients ξ, ξ(1ξ), ξ(1ξ) 2 , ... respectively. Since the weight coefficient attenuates in geometric progressions, the data samples with large coefficients approach each other, while those with small coefficient move away from each other.…”

“…Intelligent agriculture, which integrates information technology, sensing technology, and wireless communication technology, can achieve smart sensing, transmission, and analysis of the information in each link of agricultural planting. The numerous links, various plants, and diverse sensors involved in agricultural planting undermine the efficiency and reliability of data collection and storage [19][20][21][22][23].…”

Plant leaf identification system using convolutional neural network

“…Identifying plants was an essential and challenging task. Leaf shape description was the key problem in leaf identification [11], [12]. To date, several shape characteristics have been derived to explain the form of the leaf.…”