Disease Detection in Banana Leaf Plants using DenseNet and Inception Method

Abstract

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.

CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19

“…This dataset has four classes: healthy, Cordana, Sigatoka, and Pestalotiopsis. The details of the amount of data obtained are: 936 images of banana leaves with 128 images of healthy leaves; 162 images of Cordana disease; 473 images of Sigatoka disease; and finally, 173 images of Pestalotiopsis disease [13]. An example of a dataset of image data from the four classes is shown in Figure 3.…”

“…CNN also has many models that can be used, such as ResNet50, VGG-19, and many others [11], [12]. The use of CNN method has also been widely used in various kinds of research, such as several studies have been conducted by [13]- [15], and previous research used as a reference in this research is the research of Andreanov Ridhovan, Aries Suharso and Chaerur Rozikin (2022) [13].…”

“…It seeks to improve accuracy in identifying banana plant diseases using the CNN method and can be a complement and explanation of the deficiencies in previous studies [24]. Based on the formulation of the problem above, the research that will be carried out can exceed the results of previous studies [13] 1.…”

“…After successfully getting the best results from the two schemes, the final stage is preparing a report. Where this stage is carried out by making a report in a comparison of the results of the two models and the results of previous research [13] in order to get the best results.…”

Utilizing UAV Data for Neural Network-based Classification of Melon Leaf Diseases in Smart Agriculture

“…Several scholars have also implemented a series of enhancements to achieve the objectives of plant detection and counting jobs [10], [11]. Different methods were used to classify leaf diseases: DenseNet and Inception categorized four diseases for bananas, with DenseNet showing better accuracy at 84.73% [12]. Grape leaves were classified into healthy and leaf spot categories using deep forest, achieving 96.25% accuracy [13].…”