scholarly journals Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network

2021 ◽  
Vol 8 (1) ◽  
pp. 22
Author(s):  
Abdul Jalil Rozaqi ◽  
Andi Sunyoto ◽  
M rudyanto Arief
Keyword(s):  
Neural Network ◽  
Late Blight ◽  
Convolutional Neural Network ◽  
Early Blight ◽  
Batch Size ◽  
Crop Failure ◽  
Potato Leaf ◽  
Identification Process ◽  
Potato Plants ◽  
Leaf Disease

Produk pertanian kentang menjadi sangat penting karena termasuk makanan utama bagi manusia. Kentang memiliki kandungan karbohidrat yang menjadikanya sebagai makanan utama. Dalam mengelola pertanian kentang ini tentu memiliki beberapa kendala diantaranya adalah penyakit yang menyerang pada daun kentang yang jika dibiarkan akan menghasilkan produksi yang buruk atau bahkan gagal panen. Late blight dan early blight adalah penyakit yang sering ditemui pada daun kentang. Penyakit ini memiliki gejala masing-masing sehingga para petani dapat melakukan pencegahan jika melihat gejala pada daun kentang, tetapi langkah ini memliki kelemahan yaitu proses identifikasi yang lama, dan jika penanganan pada penyakit daun ini sangat lambat akan mengakibatkan penambahan biaya perawatan. Dengan memanfaatkan teknologi yaitu berupa pengolahan citra digital maka hal ini bisa diatasi, jadi pada penelitian ini akan mengusulkan metode yang tepat dalam mendeteksi penyakit pada daun kentang. Klasifikasi akan dilakukan dengan tiga kelas berupa daun sehat, early blight, dan late blight menggunakan metode Deep Learning mengguanakan arsitektur Convolutional Neural Network (CNN). Hasil pada peneltian ini dianggap baik karena pada epoch ke 10 dengan batch size 20 menghasilkan training akurasi 95% dan validation accuracy 94%.Kata Kunci—Penyakit daun kentang, late blight, early blight, identifikasi, CNNPotato agricultural products are essential because they are the leading food. Potatoes have carbohydrate content, which makes them the leading food for humans. But in carrying out this potato farming certainly has several obstacles, including the disease that attacks the potato leaves which if left unchecked will result in poor production or even crop failure. late blight and early blight are diseases that are often found in potato leaves. This disease has its own symptoms so that farmers can take precautions if they see symptoms on potato leaves, but this step has a weakness that is a long identification process, and if the handling of this leaf disease is very slow will result in additional maintenance costs. By utilizing technology in the form of digital image processing, this can be overcome, so this research will propose an appropriate method in detecting diseases in the leaves of potato plants. Classification will be carried out with three classes in the form of healthy leaves, early blight, and late blight using the Convolutional Neural Network (CNN) algorithm. The results of this research are considered good because on the 10th epoch with batch size 20 produces 95% accuracy training and 94% validation accuracy.Keywords—Potato leaf disease, late blight, early blight, identification, CNN

scholarly journals Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdul Jalil Rozaqi ◽  
Muhammad Rudyanto Arief ◽  
Andi Sunyoto
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Method Development ◽  
Classification Model ◽  
Learning Method ◽  
Potato Leaf ◽  
Leaf Disease

Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.

scholarly journals Early Prediction of Potato Leaf Diseases Using ANN Classifier

2021 ◽  
Vol 13 (0203) ◽  
pp. 129-134
Author(s):  
Kumar Sanjeev ◽  
Narendra Kumar Gupta ◽  
W. Jeberson Jeberson ◽  
Suneeta Paswan
Keyword(s):  
Neural Network ◽  
Late Blight ◽  
Early Blight ◽  
Potato Crop ◽  
Low Cost ◽  
Feed Forward Neural Network ◽  
Potato Leaf ◽  
Major Loss ◽  
Ann Classifier

Potatoes are cultivated in several states of India. Potatoes provides a low-cost energy in human diet. Potatoes are used in industry for making dried food products. Early blight and Late blight are major disease of potato leaf. It is estimated that the major loss occurred in potato yield due to these diseases. In this research, we have collected sample of potato leaf images from Plant Village dataset. This dataset contains 2152 images of potato leaf. It has 3 class of sample of Healthy Leaf, Early Blight and Late Blight. The 76 features are extracted from these images regarding color, texture and area. The extracted features are used to develop a classifier. The developed classifier is based on neural network for prediction and classification of potato image samples. The Feed Forward Neural Network (FFNN) Model is used for prediction and classification of unknown leaf. The accuracy of model is achieved 96.5%. Classifier is helpful in early and accurate prediction of the leaf diseases of potato crop.

scholarly journals Deep Convolutional Neural Network Feed Forward and Back Prop a gation (DCNN F BP) Algorithm f or Predicting Heart Disease u sing Internet o f Things

2021 ◽  
Vol 11 (1) ◽  
pp. 283-287
Author(s):  
Saranya N ◽  
◽  
Kavi Priya S ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
High Volume ◽  
Deep Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Batch Size ◽  
Feed Forward

In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP algorithm is exercised by applying forward and backward propagation algorithm. Batch size, epochs, learning rate, activation function, and optimizer are the parameters used in DCNN-FBP. The datasets are taken from the UCI machine learning repository. The performance measures such as accuracy, specificity, sensitivity, and precision are used to validate the performance. From the results, the model attains 89% accuracy. Finally, the outcomes are juxtaposed with the traditional machine learning algorithms to illustrate that the DCNN-FBP model attained higher accuracy.

A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network

2018 ◽  
Vol 61 (5) ◽  
pp. 1461-1474 ◽  
Author(s):  
Zhongqi Lin ◽  
Shaomin Mu ◽  
Aiju Shi ◽  
Chao Pang ◽  
Xiaoxiao Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Graphics Processing Units ◽  
High Efficiency ◽  
Saliency Detection ◽  
Machine Learning Algorithms ◽  
Brown Spot ◽  
Maize Leaf ◽  
Leaf Disease ◽  
Fully Connected

Abstract. Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Image classification, Machine learning algorithms, Maize leaf disease.

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