Abstract This paper investigates the issue of real-world identification to fulfill better species protection. We focus on plant species identification as it is a classic and hot issue. In tradition plant species identification the samples are scanned specimen and the background is simple. However, real-world species recognition is more challenging. We first systematically investigate what is realistic species recognition and the difference from tradition plant species recognition. To deal with the challenging task, an interdisciplinary collaboration is presented based on the latest advances in computer science and technology. We propose a novel framework and an effective data augmentation method for deep learning in this paper. We first crop the image in terms of visual attention before general recognition. Besides, we apply it as a data augmentation method. We call the novel data augmentation approach attention cropping (AC). Deep convolutional neural networks are trained to predict species from a large amount of data. Extensive experiments on traditional dataset and specific dataset for real-world recognition are conducted to evaluate the performance of our approach. Experiments first demonstrate that our approach achieves state-of-the-art results on different types of datasets. Besides, we also evaluate the performance of data augmentation method AC. Results show that AC provides superior performance. Compared with the precision of methods without AC, the results with AC achieve substantial improvement.
There are amounts of plant identification approaches that use digital images. As stated above, early algorithms are mainly with leaves. Flavia @cite_2 and Swedish leaf database @cite_26 are two typical leaf datasets. Samples of Flavia and Swedish leaf are shown in Figure . @cite_7 employed Probabilistic Neural Network(PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. Using Artificial Neural Network(ANN), @cite_13 designed a computer vision classifier to identify the different Swedish tree classes in terms of their leaves. Many methods employed shape or curvature features as plants are basically classified according to the shapes of their leaves @cite_9 @cite_17 @cite_0 @cite_31 @cite_15 . Shape or curvature features are relatively discriminative for leaf images according to the theory of plant shape taxonomy. It is efficient especially when image contents are extreme simple. Two-dimensional multifractal detrended fluctuation analysis is used for plant classification in @cite_10 .
@cite_26 @cite_7 @cite_15 @cite_10 @cite_9 @cite_0 @cite_2 @cite_31 @cite_13 @cite_17
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In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90 . Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation. Background In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I0, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4 . The discriminant model correctly classified on average: 77.9 of redroot pigweed, 93.8 of sunflower, 89.4 of velvetleaf and 96.5 of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2 . The discriminant model correctly classified 76.4 of redroot pigweed, 93.6 of sunflower, 81.6 of velvetleaf, 91.5 of soybean leaf extracted from trifoliolate and 90.9 of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping. Abstract Plant identification systems developed by computer vision researchers have helped botanists to recognize and identify unknown plant species more rapidly. Hitherto, numerous studies have focused on procedures or algorithms that maximize the use of leaf databases for plant predictive modeling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. In this paper, we learn useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gain intuition of the chosen features based on a Deconvolutional Network (DN) approach. We report somewhat unexpected results: (1) different orders of venation are the best representative features compared to those of outline shape, and (2) we observe multi-level representation in leaf data, demonstrating the hierarchical transformation of features from lower-level to higher-level abstraction, corresponding to species classes. We show that these findings fit with the hierarchical botanical definitions of leaf characters. Through these findings, we gained insights into the design of new hybrid feature extraction models which are able to further improve the discriminative power of plant classification systems. The source code and models are available at: https: github.com cs-chan Deep-Plant .