A group of plant phenomics experts and data scientists from China and Singapore have come up with a swarm intelligence algorithm for feature selection (SSAFS), which enables effective image-based plant disease detection.
A combination of two principles was utilized by the study: high-throughput phenomics, via which plant traits like disease severity could be examined on a large scale, and computer vision, in which image features representative of a particular condition have been extracted.
With the help of SSAFS and a set of plant images, the scientists determined an “optimal feature subset” of plant diseases. This subset surrounded a list of only the high-priority features that could be successful in verifying a plant as diseased or healthy.
The SSAFS’ effectiveness was tested in six plant phenomics datasets and four UCI datasets. These datasets were utilized to make a comparison of the performance of SSAFS against that of five other identical swarm intelligence algorithms. In both plant disease detection and severity estimation, the study outcomes have illustrated that SSAFS works. Certainly, it surpassed the present sophisticated algorithms in determining the highly valuable handcrafted image features.
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