A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

Julianto, Afis; Sunyoto, Andi

doi:10.11591/ijai.v10.i4.pp1069-1078

Cited by 11 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The attention mechanism helped the model to find the key attributes, while the multi-scale feature integration technology could integrate the features of different scales for comprehensive analysis. (Julianto and Sunyoto, 2021). Mendeley Data was used in the dataset, with 5,932 images covering four disease categories: white leaf blight, rice blast, brown spot, and tungro disease.…”

Section: Convolutional Neural Network For Better Recognition Accuracymentioning

confidence: 99%

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

Ning,

Liu,

Zhu

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.

show abstract

Section: Convolutional Neural Network For Better Recognition Accuracymentioning

confidence: 99%

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

Ning,

Liu,

Zhu

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) is a technology to enable machines, computers, and statistical tools and equipment to create software that can imitate human capabilities especially on the very complex tasks e.g., memories, classification, reasoning, decision, prediction, and even communication with human beings, all through algorithms. In some cases, AI can be improved through self-learning which consists of 3 levels: machine learning [1], machine intelligence [2], and machine consciousness [3]. Machine learning is one of AI capabilities which the machine can learn on its own.…”

Section: Introductionmentioning

confidence: 99%

An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture

Tangwannawit

2022

IJ-AI

View full text Add to dashboard Cite

This research focused on testing with maize, economical crop grown in Phetchabun province, Thailand, by installing a total of 20 sets of internet of things (IoT) devices which consist of soil moisture sensors and temperature and humidity sensors (DHT11). Data science tools such as rapidminer studio was used for data cleansing, data imputation, clustering, and prediction. Next, these data would undergo data cleansing in order to group them to obtain optimization clustering to identify the optimum condition and amount of water required to grow the maize through k-mean technique. From the analysis, the optimization result showed 3 classes and these data were further analyzed through prediction to identify precision. By comparing several algorithms including artificial neural network (ANN), decision tree, naïve bayes, and deep learning, it was found that deep learning algorithm can provide the most accurate result at 99.6% with root mean square error (RMSE)=0.0039. The algorithm obtained was used to write function to control the automated watering system to make sure that the temperature and humidity for growing maize is at appropriate condition. By using the improved watering system, it improved the efficacy of watering system which saves more water by 13.89%

show abstract

“…12, No. 6, December 2022: 6675-6683 6676 more efficient method of identifying plant diseases [8], [9]. Deep learning architecture, namely convolutional neural network (CNN), has shown remarkable performance in image classification [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Robusta coffee leaf diseases detection based on MobileNetV2 model

Aufar¹,

Kaloka²

2022

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Indonesia is a major exporter and producer of coffee, and coffee cultivation adds to the nation's economy. Despite this, coffee remains vulnerable to several plant diseases that may result in significant financial losses for the agricultural industry. Traditionally, plant diseases are detected by expert observation with the naked eye. Traditional methods for managing such diseases are arduous, time-consuming, and costly, especially when dealing with expansive territories. Using a model based on transfer learning and deep learning model, we present in this study a technique for classifying Robusta coffee leaf disease photos into healthy and unhealthy classes. The MobileNetV2 network serves as the model since its network design is simple. Therefore, it is likely that the suggested approach will be deployed further on mobile devices. In addition, the transfer learning and experimental learning paradigms. Because it is such a lightweight net, the MobileNetV2 system serves as the foundational model. Results on Robusta coffee leaf disease datasets indicate that the suggested technique can achieve a high level of accuracy, up to 99.93%. The accuracy of other architectures besides MobileNetV2 such as DenseNet169 is 99.74%, ResNet50 architecture is 99.41%, and InceptionResNetV2 architecture is 99.09%.</span>

show abstract

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

Cited by 11 publications

References 16 publications

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture

Robusta coffee leaf diseases detection based on MobileNetV2 model

Contact Info

Product

Resources

About