Abstract
The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.
Highlights
Chili peppers are plants used and consumed by Thai people in many forms
The chili pepper disease diagnosis model was developed by applying filter and wrapper methods along with Multi-Layer Perceptron Neural Networks (MLPNNs)
The research results showed that the model developed by Wrapper and MLPNN (Wrapper+MLP) provided the highest results with accuracy of 98.91%, precision of 98.92%, and recall of 98.89%
Summary
Chili peppers are plants used and consumed by Thai people in many forms. They are fundamental spices that can enhance the flavor, odor, and color of food. Many farmers usually face problems in chili pepper plantations. These include inevitable natural disasters, weeds, and pests. The farmers need to be educated about chili pepper diseases in order to protect their plants appropriately. Multi-Layer Perceptron Neural Networks (MLPNNs) have been broadly applied to diagnose diseases. Such models were developed in [2] and in [3] for predicting lung cancers and heart diseases respectively. These two studies developed disease diagnosis models which were more than 90% effective
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