Analysis of Lime Leaf Disease using Deep Learning
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Abstract
Currently, lime is a type of plant that has been cultivated in many places. Because limes are used in cooking, and their properties are herb. In Thailand, limes are used for drinking and medical herb to used in health care. From this popularity, the cultivation of limes became widespread and began to be planted more in farm and more at home. Nowadays, lime cultivation can be controlled to produce off-season produce. However, lime is also prone to diseases without proper care. For these reasons, this research is to study the analyzation of decease from lime leaves by using deep learning. The Convolutional Neural Networks (CNNs) of deep learning is used to classify the decease. The proposed architecture of CNNs in this study is to compare to LeNet-5, VGG16, and RestNet-50 architectures. The total number of single lime leaf images is 5,710. The input images are RGB color. The normal and decease lime leaves are separated in equal. Training and test sets are 80 and 20 percent, respectively. The evaluation result is found that LeNet-5 has the lowest accuracy, while the proposed architecture has the highest accuracy but it is not different from ResNet-50.
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