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.
Highlights
Wheat is an important primary food for a large proportion of the world’s population, so methods to estimate its yield have received significant research attention (Bognár et al, 2017)
The specific algorithm is as follows: Step 1: Select N images as training samples and extract patches of a certain size (20 × 20) from these samples; Step 2: Extract the color feature, texture feature, and edge histogram descriptor feature from the samples; Step 3: Use kernel principal component analysis (KPCA) to extract the principal component features and calculate the weight for each feature in each class of samples; Step 4: Train the TWSVM classification model with the weighted features updated in Step 3; Step 5: Perform a weighting to the feature in the test sample with feature weights in each class, use the TWSVM-Seg model obtained in Step 4 to classify, and determine the image segmentation (Figure 3)
Currency denomination and detection is an application of image segmentation
Summary
Wheat is an important primary food for a large proportion of the world’s population, so methods to estimate its yield have received significant research attention (Bognár et al, 2017). The number of ears per unit area is mainly obtained in the field. An accurate determination of the number of ears is vital for estimating wheat yield and is a key step in field phenotyping (Zhang et al, 2007). Two main statistical methods exist to obtain the number of ears per unit area: manual field investigation and image-based crop recognition (Nerson, 1980). In the field of image segmentation, a number of meaningful research results have emerged in recent years. These methods mostly focus on two approaches, the first of which is based solely on color information (Naemura et al, 2000). In addition to the disadvantages described above, an excessive dependence on color information will lead to incomplete extraction
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