scholarly journals Detection of Rice Plant Diseases using Convolutional Neural Network

2021 ◽  
Vol 1125 (1) ◽  
pp. 012021
Author(s):  
I Y Purbasari ◽  
B Rahmat ◽  
C S Putra PN
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rice Plant ◽  
Plant Diseases

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 A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

scholarly journals Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 328
Author(s):  
Sachin B. Jadhav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Plant Diseases ◽  
Experimental Results ◽  
Disease Classification ◽  
Soybean Leaves ◽  
Soybean Diseases ◽  
Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

Plant Disease Detection and Classification Using Bacteria Foraging Optimization Algorithm Through Convolution Neural Network

2020 ◽  
Vol 17 (8) ◽  
pp. 3567-3576
Author(s):  
Venigalla Sai Teja ◽  
Chilakapati Srinivas ◽  
P. Radhika
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Euclidean Distance ◽  
Plant Diseases ◽  
Prompt Action ◽  
The Right ◽  
Measurement Metric ◽  
Bacteria Foraging Optimization Algorithm ◽  
Advanced Model

Humans can recognize the plants infected by diseases but separated from our visual perception it is hard to recognize plant diseases. In croplands without taking the right care and prompt action, the entire field may become a region afflicted by diseases. So we identify the plant diseases ahead of time with the assistance of present-day computer technologies. An advanced model was introduced to accurately recognize and classification plant diseases. Here we proposed an approach that can use the Convolutional Neural Network (CNN) based on BFOA for distinguishing diseases in plants. The input picture for the extraction of features is divided into 3 clusters by the Euclidean distance measurement metric of the k-mean algorithm and from the ROI, parameters of the GLCM matrix are calculated in the same cluster prior to BFOA. Assigning matrix parameters as BFOA input improves the network’s accuracy and efficiency in determining. In classification, we proposed a Convolutional Neural Network (CNN) using ResNet50 as a pre-trained network in deep learning toolbox which classifies from a given dataset. The approach is more reliable as the detection and classification of plant diseases are more precise.

scholarly journals Detection of Tomato Plant Diseases Using Deep Convolutional Neural Network

2020 ◽  
pp. 464-465
Author(s):  
Vijayaganth V ◽  
Naveenkumar M ◽  
Mohan M
Keyword(s):  
Neural Network ◽  
Early Diagnosis ◽  
Convolutional Neural Network ◽  
Tomato Plant ◽  
Learning Rate ◽  
Deep Convolutional Neural Network ◽  
Plant Diseases ◽  
Batch Size ◽  
Diagnosis Of Diseases

The disease in tomato leaves affects the quality and quantity of the crops. To overcome this problem an early diagnosis of diseases will benefit the farmers. This work uses PlantVillage dataset of 9 tomato leaves and fed to AlexNet and VGG16. It focuses on accuracy of the model by using hyperparameters like batch size, learning rate and optimizer.

scholarly journals A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

2020 ◽  
Vol 37 (6) ◽  
pp. 1093-1101
Author(s):  
Divakar Yadav ◽  
Akanksha ◽  
Arun Kumar Yadav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Daily Intake ◽  
Plant Diseases ◽  
Data Set ◽  
Proposed Model ◽  
Fcm Clustering ◽  
Contrast Stretching

Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm.

scholarly journals RICE PLANT DISEASE CLASSIFICATION USING TRANSFER LEARNING OF DEEP CONVOLUTION NEURAL NETWORK

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 631-635 ◽  
Author(s):  
V. K. Shrivastava ◽  
M. K. Pradhan ◽  
S. Minz ◽  
M. P. Thakur
Keyword(s):  
Neural Network ◽  
Rice Plant ◽  
Plant Disease ◽  
Crop Protection ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Classification ◽  
Real Field ◽  
Support Vector ◽  
Identification System

<p><strong>Abstract.</strong> Early and accurate diagnosis of plant diseases is a vital step in the crop protection system. In traditional practices, identification is performed either by visual observation or by testing in laboratory. The visual observation requires expertise and it may vary subject to an individual which may lead to an error while the laboratory test is time consuming and may not be able to provide the results in time. To overcome these issues, image based machine learning approach to detect and classify plant diseases has been presented in literature. We have focused specifically on rice plant (<i>Oryza sativa</i>) disease in this paper. The images of the diseased symptoms in leaves and stems have been captured from the rice field. We have collected a total of 619 rice plant diseased images from the real field condition belong to four classes:(a) Rice Blast (RB), (b) Bacterial Leaf Blight (BLB), (c) Sheat Blight (SB) and (d) Healthy Leave (HL). We have used a pre-trained deep convolutional neural network(CNN) as a feature extractor and Support Vector Machine (SVM) as a classifier. We have obtained encouraging results. The early identification of rice diseases by this approach could be used as a preventive measure well as an early warning system. Further, it could be extended to develop a rice plant disease identification system on real agriculture field.</p>

scholarly journals Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5569
Author(s):  
Jamil Ahmad ◽  
Bilal Jan ◽  
Haleem Farman ◽  
Wakeel Ahmad ◽  
Atta Ullah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Plant Diseases ◽  
Field Conditions ◽  
Disease Detection ◽  
Resource Constrained ◽  
Extensive Data ◽  
Resource Constrained Devices ◽  
Constrained Devices

The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices.

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