Abstract
To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram–Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper.
Highlights
Wheat yield is an important part of national food security [1], and spikes per unit area is an important factor in wheat yield
Because the brightest firefly XB cannot be attracted by other fireflies, its motion cannot be described by Equation (1), so we propose the following: XB(t + 1) = XB(t) + α(r2 − 0.5)Xm where t represents evolutionary algebra and r2 is a random number uniformly distributed over [0,1]
Multispectral and Panchromatic Image Fusion Results Based on the Gram–Schmidt Algorithm
Summary
Wheat yield is an important part of national food security [1], and spikes per unit area is an important factor in wheat yield. With the continuous improvement in the mechanization and digitalization of agricultural production, the methods of predicting crop production have gradually diversified, and many methods are available for small area production forecasting These methods include field artificial prediction, capacitance measurement, climate analysis and prediction, remote sensing prediction, and prediction of the year’s harvest [2]. An automated method for predicting the yield of cereals, especially of wheat, is highly desirable because its manual evaluation is excessively time consuming To address this issue, we propose to use image processing methods to count the number of wheat spikes per square meter, thereby simplifying the work of agriculture technicians. The severe adhesion between wheat ears is so serious that it is impossible to obtain accurate sharp information [13] Based on these considerations, the color feature is used as the basis for image segmentation.
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