Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

Abstract

The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings.

Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network

“…There is no doubt that image-based techniques provide a feasible, low-cost, and efficient solution for crop counting, such as wheat ear counting [17,18], oilseed rape flower counting [7], and sorghum head counting [19]. However, because of the complexity of the field environment (e.g., illumination intensity, soil reflectance, and weeds, which alter colors, textures, and shapes in crop UAV images) and the diversity of image capture parameters (e.g., shooting angle and camera resolution, which can easily cause crops to be blurred in UAV images), accurate crop counting remains an enormous challenge.…”

Automatic Wheat Ear Counting Using Thermal Imagery

“…Ear density can be used as a target breeding trait in cereal phenotyping. To date, the few studies dealing with automatic ear counting in the field have mostly been performed using RGB images [6,[8][9][10][11][12][13]. Besides the intrinsic low cost of this approach due to the easy operation and affordability of digital cameras, the high resolution of the natural color digital images is a major factor to consider as both a cost and a benefit.…”

“…In the manual in situ counting in the field, it was necessary to both view the canopy from different angles as well as physically move plants to acquire accurate field validation data, representing a major difference between the in situ counting and the single image-perspective remote sensing approach of the automatic thermal image ear counting technique presented here. In previous studies on ear recognition, no information regarding the correlation between in situ visual ear counting and automatic ear counting was provided [6][7][8][9][10][11][12][13][14], but it is nonetheless an important point to consider as the entire image acquisition and processing pipeline represents a sum of errors. Of course, the approach for visual counting assayed was in fact much faster than the traditional ear counting procedures, which implies for example counting the total number of ears in one-meter linear row length.…”

“…As an alternative, several automatic ear counting techniques have been developed in the last years, mainly using as input high resolution RGB (Red/Green/Blue) images. Different image processing techniques have been used such as texture and hybrid color space [6,7], multi-features from color, grayscale and texture data [8]. Decorrelation stretch for color contrast enhancement and Support Vector Machine (SVM) as classification techniques [9,10] and convolutional neural network recognition have been also used [11].…”

Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging

“…The researchers applied all color components to train a support vector machine (SVM) model that can detect wheat spikes. Zhou et al [26] proposed an approach using a color feature (color coherence vectors), a texture feature (gray level co-occurrence matrix), and an image feature (edge histogram descriptor) to train a twin SVM model. Thus, this successfully extracted each spike pixel from the background.…”