Local descriptors for soybean disease recognition

Highlights

• A novel approach is proposed for soybean disease recognition using leaf images.

• It is based on local descriptors and bag-of-visual words.

• Experimental results on an image dataset with 1200 samples validate its effectiveness.

• Results also show that color increases the correct classification rate.

Abstract

The detection of diseases is of vital importance to increase the productivity of soybean crops. The presence of the diseases is usually conducted visually, which is time-consuming and imprecise. To overcome these issues, there is a growing demand for technologies that aim at early and automated disease detection. In this line of work, we introduce an effective (over 98% of accuracy) and efficient (an average time of 0.1 s per image) method to computationally detect soybean diseases. Our method is based on image local descriptors and on the summarization technique Bag of Visual Words. We tested our approach on a dataset composed of 1200 scanned soybean leaves considering healthy samples, and samples with evidence of three diseases commonly observed in soybean crops – Mildew, Rust Tan, and Rust RB. The experimental results demonstrated the accuracy of the proposed approach and suggested that it can be easily applied to other kinds of crops.

Introduction

Soybean is one of the most important crops due to its beneficial effects on human health, to its role as a major nutrition source, and to its economic importance. It has been widely used in food and industrial applications because of its high protein and oil concentrations (Kumar et al., 2010). Soybean occupies very large crops in which the monocropping and conservation tillage are commonly used. Such cultivation systems, however, have favored the occurrence of a large number of diseases (Carmona et al., 2015) causing major economic losses. The solution is to apply preventive agrochemicals; but, because the identification of where the infestation took place is time-consuming, the usual practice is to use agrochemicals over the entire crop instead of only over specific subareas. This is an expensive practice that spreads unnecessary chemicals over terrain and air.

Accordingly, a more precise detection of the disease spots in the crop is an important step to decrease economic losses, to prevent the spread of diseases, and to reduce environmental pollution. Despite its importance, it is usually conducted visually by an expert (Moshou et al., 2004), an imprecise and time-consuming process, especially when carried out over large-scale farms. Alternatively, disease detection techniques based on chemical reactives are available, such as the ELISA (enzyme-linked immunosorbent assay) method and the PCR (polymerase chain reaction) method (Saponari et al., 2008, Yvon et al., 2009, Gutiérrez-Aguirre et al., 2009), however, they are expensive processes. Consequently, there is a demand for rapid and cheaper detection methods.

In this context, one active line of research is the use of image processing techniques. The idea is to have the computer analyze images of soybean leaves (and of other cultures) to detect diseases by means of pattern recognition methods. Gui et al. (2015), for example, proposed a method for soybean disease detection based on salient regions and k-means clustering. Shrivastava and Hooda (2014) proposed a method for detecting brown spot and frog eye, two common soybean diseases; they used shape features and k-nearest neighbors classification. Ma et al. (2014) proposed a technique for detecting insect-damaged vegetable soybean using hyperspectral imaging. A study to discriminate soybean leaflet shape using neural networks was proposed in the work of Oide and Ninomiya (2000). Yao et al. (2012) used hyperspectral images to study the damage caused by the herbicide glyphosate on soybean plants. Cui et al. (2010) reported image processing techniques for quantitatively detecting rust severity from soybean multi-spectral images.

Besides soybean, other cultures have been studied in the literature, such as the work performed by Rumpf et al. (2010), which presents an automatic system for classification of foliar sugar beet diseases based on Support Vector Machines and spectral vegetation indices. Moshou et al. (2004) investigated the automatic recognition of yellow rust in wheat using reflectance measurements and neural networks. Liu et al. (2010) applied techniques neural network and principal components analysis for classifying fungal infection levels in rice panicles. Imaging techniques are also applied in the recognition of plant species (Gonçalves and Bruno, 2013, Casanova et al., 2012, Backes et al., 2010). A review of techniques for detecting plant diseases can be found in the work of Sankaran et al. (2010); a survey on methods that use digital image processing techniques to detect plant diseases is presented in the work of Barbedo (2013).

This paper proposes a novel approach for soybean disease recognition based on techniques local descriptors and bag-of-visual words. We experiment with five local descriptors (SURF, HOG, DSIFT, SIFT, and PHOW, as detailed in Section 3) applied over a large set of digital images (gray scale and colored) acquired from a real-world soybean plantation in Brazil. The proposed approach is applied to scanned images (visible spectrum to the human eye), which does not require hyperspectral images and, therefore, can be used with commodity hardware such as smartphones. From the extracted features, we calculate a summary (lower-dimensional) feature vector using technique bag of visual words (BOVW).

The use of local descriptors is attractive because they are distinctive, robust to occlusion, and do not require segmentation. Due to these advantages, several local descriptors have been proposed in the literature. Scale-invariant feature transform (SIFT) (Lowe, 2004) is one of the most used and known local descriptors. Due to its good performance, SIFT was later applied at dense grids, known as Dense SIFT (Vedaldi and Fulkerson, 2010, Liu et al., 2011), and at multiscale dense grids, known as Pyramid histograms of visual words (PHOW) (Bosch et al., 2007). SIFT also inspired other local descriptors such as Speeded-up Robust Features (SURF) (Bay et al., 2008), which uses some approximations to achieve better performance. Histogram of oriented gradients (HOG) (Dalal and Triggs, 2005) has also been widely used and has shown interesting results, especially in pedestrian recognition. The reader may refer to the work of Mikolajczyk and Schmid (2005) for a review of local descriptors applied over images in general.

For classification purposes – considering classes disease and no disease, we use the supervised machine learning technique Support Vector Machine (SVM) having as input the BOVW vectors. We evaluate our classification using classic ten-fold cross-validation and the metric Correct Classification Rate (CCR). In our experiments, descriptor PHOW, over colored images, achieved the highest performance. Therefore, we contribute by (i) introducing a systematic method for computational identification of diseases in soybean leaves; (ii) conducting an experiment over soybean that is unprecedented in its control, methodology, and scale; (iii) empirically comparing the main local descriptors found in the literature, providing guidance for future works on image-based classification.

The rest of this paper is organized as follows. Section 2 describes the fundamentals of bag-of-visual-words. The five local descriptors evaluated in this work are described in Section 3. Section 4 details the experimental design and the image dataset of soybean leaves, while Section 5 describes the results of the proposed approach. Finally, Section 6 concludes the paper and suggests future works.