An Analysis of Pineapple Leaf Wilt by Image Processing and Convolutional Neural Network
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Abstract
This research presented a method for applying digital photographs to agriculture for the analysis of pineapple leaf wilt by using digital photographs. There were 3 steps in the research as follows: step 1: Input Image, step 2: Train Image, and step 3: System Test. The research used digital photos taken with a smartphone that the camera had a resolution of 12 megapixels. All 865 digital photos were brought into a system developed for practicing learning. The images imported into the system were divided into 4 classes: full leaf class, tip class, mid leaf class, and base leaf class. The machine was set to learn repeatedly for 10 rounds in the process of testing the system. The researchers used unprocessed digital photographs and used them to test the correctness of the system. The research revealed an analysis of pineapple leaf wilt by digital image processing and a convolutional neural network, it was able to analyze photos with wilt of pineapple leaves with an accuracy of 98.96%.
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References
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