Abstract
In this paper, an algorithm to classify leaf disease severity based on lesions is presented. The algorithm involved three main steps, filtration, recognition and detection.Artificial Bee Colony, Fuzzy Logic, Otsu and Geometry formula were incorporated to achieve the goal.Ninety-four leaf images were used in this algorithm combination experiment.The study was conducted in four phases, filtration, recognition, detection and evaluation.Comparison was made with four other algorithms, Otsu, Canny, Robert and Sobel. Results showed that the Leaf Lesion Classification (LLC) algorithm based on Artificial Bee colony (ABC) produced an average 96.83% of accuracy and average 1.66 milliseconds of processing time, indicating that LLC algorithm is better than algorithm such as Otsu, Canny, Roberts and Sobel. The study makes a substantial contribution to the body of knowledge in image processing.
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Claims 3
Claim · model-written summary
LLC achieved average accuracy of 96.83% for leaf lesion classification.
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e0“Leaf Lesion Classification (LLC) algorithm based on Artificial Bee colony (ABC) produced an average 96.83% of accuracy”
Claim · model-written summary
LLC averaged 1.66 milliseconds processing time.
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e0“average 1.66 milliseconds of processing time”
Claim · model-written summary
LLC performed better than Otsu, Canny, Roberts and Sobel.
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e0“LLC algorithm is better than algorithm such as Otsu, Canny, Roberts and Sobel”