scholarly journals Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN)

2020 ◽  
Vol 3 ◽  
pp. 393-396
Author(s):  
Achmad Ramadhanna’il Rasjava ◽  
Aditya Wisnugraha Sugiyarto ◽  
Yori Kurniasari ◽  
Syaifullah Yusuf Ramadhan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Rice Plant ◽  
Leaf Blight ◽  
Rice Production ◽  
Training Data ◽  
Brown Spot ◽  
Bacterial Leaf Blight ◽  
Rice Plants

As a rice-producing plant, rice plant (Oryza sativa L.) is one of the most important crops in Indonesia. Rice production is increasing every year along with an increase in rice demand and population.The amount of rice production is affected by the condition of the rice plants. The worse the condition of rice plants, the rice production will also lower. Rice plant is very susceptible to diseases or pests that can reduce its productivity, including brown spot disease, leaf smut and bacterial leaf blight. As the development of science and technology, currently known as Artificial Intelligence. Artificial intelligence is a combination of several scientific disciplines such as mathematics, statistics, computer science, and even social science. Using artificial intelligence, the system now have the ability to interpret external data correctly to learn from the data and then use the learning to achieve certain goals through flexible adaptation. The artificial intelligence fields consists of several branches, such as machine learning and deep learning. Neural Network (NN) is one of the methods used in the deep learning.NN has many types, one of which is the Convolutional Neural Network (CNN). CNN is the best-knownmethod used for processingimages data compared to other types of NN. Therefore, in this study the identification of rice plants diseases was carriedout using CNN method. From this study,better results were obtained compared to other methods, obtaining 100% accuracy for training data and 86,67% for testing data. The model obtained by the CNN method can be used for detecting 3 different types of rice plants diseases, there are brown spots, leaf smuts, or bacterial leaf blight disease based on the physical images of rice plant leaves.

scholarly journals Penerapan Algoritma Convolutional Neural Network Dan Arsitektur MobileNet Pada Aplikasi Deteksi Penyakit Daun Padi

Swabumi ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 184-188
Author(s):  
Rizal Amegia Saputra ◽  
Sri Wasiyanti ◽  
Adi Supriyatna ◽  
Dede Firmansyah Saefudin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Leaf Blight ◽  
Brown Spot ◽  
Bacterial Leaf Blight ◽  
Leaf Smut

Padi merupakan tanaman pangan penghasil beras, dan indonesia merupakan negera yang mayoritas penduduknya menjadikan beras sebagai makanan utama, jumlah penduduk yang semakin meningkat, perlu menjaga kualitas padi agar resiko gagal panen dapat dihindari. Banyak faktor yang dapat menimbulkan resiko gagal panen salahsatunya itu penyakit daun padi, Pada penelitian ini diusulkan Algoritma Convolutional Neural Network untuk klasifikasi penyakit daun padi yang berdasarkan citra. Arsitektur yang digunakan pada penelitian ini menggunakan MobileNetVI dengan menggunakan ekstraksi fitur. Dataset berasal dari UCI Repository sebanyak 120 yang terdiri dari 3 penyakit daun padi yaitu Bacterial leaf blight,  Brown spot, Leaf smut. Berdasarkan hasil pelatihan dan pengujian menggunakan citra penyakit daun padi yang berukuran 224x224 piksel didapat hasil nilai akurasi pelatihan mencapai 1.0 dan nilai akurasi validasi mencapai 0.8333. Nilai akurasi pada Confusion Matrix yaitu sebesar 92%, hasil ini menjadi bukti bahwa dengan penerepan algorima CNN dan MobileNetVI dengan ekstraksi ciri memiliki akurasi yang baik sekali. Percobaan pada aplikasi yang dibangun hasil proses pengujian berbasis android terbukti dapa mengklasifikasikan jenis penyakit daun padi.

scholarly journals Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

2021 ◽  
Vol 12 (2) ◽  
pp. 123
Author(s):  
A A JE Veggy Priyangka ◽  
I Made Surya Kumara
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Rice Plant ◽  
Data Augmentation ◽  
Rice Production ◽  
Plant Diseases ◽  
Test Accuracy ◽  
Neural Network Method ◽  
Network Method

Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.

scholarly journals Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1688
Author(s):  
Luqman Ali ◽  
Fady Alnajjar ◽  
Hamad Al Jassmi ◽  
Munkhjargal Gochoo ◽  
Wasif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Concrete Structures ◽  
Model Performance ◽  
Training Data ◽  
Computational Time ◽  
Data Heterogeneity ◽  
Public Datasets ◽  
Detection And Localization

This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.

scholarly journals Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
Protein Function ◽  
Protein Localization ◽  
Convolutional Network ◽  
High Throughput Analysis ◽  
Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

scholarly journals Detection of Strawberry Diseases Using a Convolutional Neural Network

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Jia-Rong Xiao ◽  
Pei-Che Chung ◽  
Hung-Yi Wu ◽  
Quoc-Hung Phan ◽  
Jer-Liang Andrew Yeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Powdery Mildew ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Cost Effective ◽  
Gray Mold ◽  
Leaf Blight ◽  
Crown Rot ◽  
Accuracy Rate ◽  
Fruit Disease

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

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