Original papers
Leaf image based cucumber disease recognition using sparse representation classification

https://doi.org/10.1016/j.compag.2017.01.014Get rights and content

Highlights

  • The log frequency histogram features are extracted from each lesion image.

  • The lesion shape features are combined with color features.

  • The sparse coefficients can explain the importance of each disease leaf image.

  • The cucumber disease is recognized by SRC.

Abstract

Most existing image-based crop disease recognition algorithms rely on extracting various kinds of features from leaf images of diseased plants. They have a common limitation as the features selected for discriminating leaf images are usually treated as equally important in the classification process. We propose a novel cucumber disease recognition approach which consists of three pipelined procedures: segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying diseased leaf images using sparse representation (SR). A major advantage of this approach is that the classification in the SR space is able to effectively reduce the computation cost and improve the recognition performance. We perform a comparison with four other feature extraction based methods using a leaf image dataset on cucumber diseases. The proposed approach is shown to be effective in recognizing seven major cucumber diseases with an overall recognition rate of 85.7%, higher than those of the other methods.

Introduction

Crop diseases have critical effects on the quality and quantity of agricultural products by destroying the normal state of the crop and change or interrupt crop vital functions such as photosynthesis, transpiration, pollination, fertilization, germination (Wang et al., 2015). Accurate recognition and diagnosis of crop diseases at early stage is very important (Gavhale and Ujwalla, 2014, Rumpf et al., 2010). The symptoms of crop diseases often appear visually on plant leaves, thus it is possible to automatically detect crop diseases by applying machine learning techniques on leaf images (Camargo and Smith, 2009, Hillnhuetter and Mahlein, 2008). Specifically, crop diseases can be classified and recognized by analyzing color, texture, and shape of the diseased leaf images (Al-Hiary et al., 2011, Gulhane and Gurjar, 2011, Patil and Kumar, 2011). Using features extracted from leaf images, many crop disease recognition methods and systems have been developed (Chaudhary et al., 2012, Al-Bashish et al., 2011). For example, Pixia and Xiangdong implemented several leaf image processing and recognition technologies to study the cucumber disease of downy mildew, powdery mildew, and anthracnose (Pixia and Xiangdong, 2013). Kulkarni et al. used the Gabor filter to extract features and applied artificial neural networks (ANN) to classify leaf images (Kulkarni and Ashwin Patil, 2012). Revathi et al. used a homogeneous pixel counting technique for cotton disease recognition (Revathi and Hemalatha, 2012). Al-Tarawneh applied auto-cropping segmentation and fuzzy C-means classification to analyze leaf color in the olive leaf spot disease (Mokhled, 2013). Kanjalkar and Lokhande proposed to use leaf features extracted from the segmented disease region for disease detection (Kanjalkar and Lokhande, 2014).

Despite the considerable efforts, the existing crop disease recognition methods are not adequate or sufficient due to two limitations: (1) the features extracted from the leaf images of crop disease are in general sensitive to the illumination, orientation and scaling of the images, therefore a preprocessing step prior to feature extraction is often required to account for various translation, rotation, and scaling factors; (2) the features selected for classification are usually treated as equally important, regardless of their actual roles in the classification process. In reality, some features may have little contribution or even have confounding effect to the disease recognition. Assigning equal weights to all the features tends to result in inflated classification error.

SR based classification (SRC) or sparse coding aims to search for the most compact representation of the input sample in terms of linear combination of a small number of elementary samples called atoms, which are usually selected from an over-complete dictionary (Wright et al., 2009). As a state-of-art machine learning technique, in recent years, SRC has been widely used in signal analysis, radar signal formation, image/video compression and reconstruction, and so on. Typical applications include face recognition, human movement recognition, tumor classification, and plant species identification (Wagner et al., 2012, Gkalelis et al., 2008, Zheng et al., 2011, Jin et al., 2015). In this paper, we propose to use SRC for cucumber disease recognition. Noticing that the disease class can be determined by its color and shape features, we combine the log spectrum of the color histogram and the lesion shape of the lesion images as our classification features, and recognize the cucumber disease according to the sparse coefficients. Our proposed method differs from the classical cucumber disease recognition methods in that the recognition is based on both shape features and color features, and the classification features receive unequal weights.

The rest of this paper is organized as follows. Section 2 introduces SRC. Section 3 describes the general procedure of lesion segmentation for cucumber leaves. In Section 4 we propose a recognition method for cucumber diseases based on SR. Experimental results and analyses are given in Section 5. Section 6 concludes the paper and points out our future work on improving the cucumber disease recognition algorithm.

Section snippets

Sparse representation based classification

SR based classification (SRC) method assumes that the training samples from a single class lie on a subspace (Wright et al., 2009, Wagner et al., 2012). As a result, the test sample can be represented as a sparse linear combination of the training samples. This representation is naturally sparse if the number of the training classes is reasonably large.

Suppose that we collect n training samples X=[x1,x2,,xn] which belong to k classes, where xiRm,i=1,2,,n, and generally mn. Let ni be the

Cucumber leaf image lesion segmentation

Lesion segmentation of cucumber leaf images is a key process in disease recognition (Jaware et al., 2012, Arivazhagan et al., 2013). Typical algorithms such as the K-means clustering can be applied to leaf lesion segmentation, which tries to classify pixels into K classes based on a set of features (Valliammal and Geethalakshmi, 2012, Chitade and Katiyar, 2010). In this paper, we use the K-means clustering algorithm to segment lesion from diseased leaf images. The steps are listed as follows.

Cucumber leaf disease recognition based SR

According to the basic principle of SR, we propose a cucumber leaf disease recognition method as follows.

Step 1 Dataset preparation. Segment the lesion from each diseased leaf image by K-means clustering algorithm.

Step 2 Color feature extraction. Divide the lesion image into three L∗a∗b∗ components, extract histograms of the L∗a∗b∗ components, calculate their 128-point fast Fourier transform (FFT), and extract their log frequency histogram features, denoted as VL,Va,Vb, respectively.

Step 3

Experiments and analysis

To evaluate the performance of the proposed algorithm, we conduct extensive experiments using a leaf image database for cucumber diseases, and compare with four feature extraction based crop leaf disease recognition methods: Support Vector Machines (SVM) (Rumpf et al., 2010), K-means-based segmentation followed by neural-network-based classification (KMSNN) (Al-Bashish et al., 2011), texture feature (TF) based classification (Arivazhagan et al., 2013), and plant leaf image (PLI) based

Conclusions and future works

Fast, automated, image-based crop disease recognition plays an important role in crop disease management. Due to the irregularity, complexity, and diversity of diseased cucumber leaves, many existing classifiers cannot meet the needs of an automated cucumber disease recognition system. Using SR, a newly developed but widely used data representation model, we propose a novel crop disease recognition method based on cucumber leaves. The proposed method uses combined shape and color features from

Acknowledgement

This work is partially supported by China National Natural Science Foundation under grant Nos. 61473237 and 61309008. It is also supported by the Shaanxi Natural Science Foundation Research Project under grant No. 2014JM2-6096. The authors would like to thank all the editors and anonymous reviewers for their constructive advices.

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