Identifikasi Jenis Penyakit pada Tanaman Cabai Merah Menggunakan Algoritma Convolutional Neural Network dalam Meningkatkan Hasil Panen Petani
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Sari, Willis Aprieta
Haris, Abdul
Kuswardani, Dwina
Haris, Abdul
Kuswardani, Dwina
Subject
Teknik Informatika
Datestamp
2022-09-26 06:27:15
Abstract :
The problem that is often faced by red chili farmers is plant-disturbing organisms as a cause of red chili plant disease. Plant diseases can cause plants to rot and die by fungi, causing crop failure which results in decreased red chili production. Sometimes some diseases are very difficult to recognize by farmers using their eyes or without tools. To find out the characteristics of these plants being attacked by diseases, of course, there are special characteristics that are displayed by plants, and not just anyone can know these characteristics. The characteristic that is most easily recognized when a plant is sick is when a symptom or other sign appears on the body of the plant caused by the organism. This solution can take advantage of computational techniques such as object recognition models using deep learning, namely Convolutional Neural Network. Convolutional Neural Network. Convolutional Neural Network is a type of neural network that is used for image data. This study used eight classes of leaf types, namely healthy, anthracnose, cercospora leaf spot, phytoftora leaf blight, fusarium wilt, choanephora leaf rot, stemfilium gray spot, and powdery mildew. In the process of training the network, it produces 71% accuracy of the training data. The testing process of validation data produces an accuracy of 25%. These results indicate that the Convolutional Neural Network method has the potential for an automatic object detection approach in distinguishing types of red chili plant diseases caused by fungi
The problem that is often faced by red chili farmers is plant-disturbing organisms as a cause of red chili plant disease. Plant diseases can cause plants to rot and die by fungi, causing crop failure which results in decreased red chili production. Sometimes some diseases are very difficult to recognize by farmers using their eyes or without tools. To find out the characteristics of these plants being attacked by diseases, of course, there are special characteristics that are displayed by plants, and not just anyone can know these characteristics. The characteristic that is most easily recognized when a plant is sick is when a symptom or other sign appears on the body of the plant caused by the organism. This solution can take advantage of computational techniques such as object recognition models using deep learning, namely Convolutional Neural Network. Convolutional Neural Network. Convolutional Neural Network is a type of neural network that is used for image data. This study used eight classes of leaf types, namely healthy, anthracnose, cercospora leaf spot, phytoftora leaf blight, fusarium wilt, choanephora leaf rot, stemfilium gray spot, and powdery mildew. In the process of training the network, it produces 71% accuracy of the training data. The testing process of validation data produces an accuracy of 25%. These results indicate that the Convolutional Neural Network method has the potential for an automatic object detection approach in distinguishing types of red chili plant diseases caused by fungi
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