Disease Detection in Banana Leaf Plants using DenseNet and Inception
Method

Disease Detection in Banana Leaf Plants using DenseNet and Inception
Method

Deep learning, Disease Detection, DenseNet, Inception

Diseases that attack banana plants can affect the growth and productivity of the fruit produced. The disease can be identified
by looking at changes in the pattern and color of the leaves. Infected leaves will experience an increased transpiration process
and the photosynthesis process is almost non-existent. Furthermore, disease on banana leaves can cause yield losses of up to
50%. Therefore, early detection is needed so that diseases on banana leaves can be overcome as soon as possible by using
deep learning. This study aims to compare the performance of DenseNet and Inception methods in detecting disease on banana
leaves. DenseNet is a transfer learning architecture model with fewer parameters and computations to achieve good
performance. Inception, on the other hand, is a transfer learning architectural model that applies cross-channel correlation,
executes at lower resolution inputs, and avoids spatial dimensions. In conducting the test, this study uses several data handling
schemes to test the two methods, namely without data handling, under-sampling, and oversampling. Furthermore, the data is
separated into training data and test data with a ratio of 80:20. The result is that the model using the DenseNet method with
an oversampling scheme is superior to other models with a percentage value of 84.73% accuracy, 84.80% precision, 84.73%
recall, and 84.62% f1 score. In addition, the machine learning model using the DenseNet method in all schemes is also superior
to the machine learning model using the Inception method.

Andreanov Ridhovan1
, Aries Suharso2
, Chaerur Rozikin3

University of Singaperbangsa Karawang

01-10-2022

Fajar bagus W

PDF

Indonesia

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