Learning Deep Transferability for Several Agricultural Classification Problems
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 1
Abstract
This paper addresses several critical agricultural classification problems, e.g. grain discoloration and medicinal plants identification and classification, in Vietnam via combining the idea of knowledge transferability and state-of-the-art deep convolutional neural networks. Grain discoloration disease of rice is an emerging threat to rice harvest in Vietnam as well as all over the world and it acquires specific attention as it results in qualitative loss of harvested crop. Medicinal plants are an important element of indigenous medical systems. These resources are usually regarded as a part of culture’s traditional knowledge. Accurate classification is preliminary to any kind of intervention and recommendation of services. Hence, leveraging technology in automatic classification of these problems has become essential. Unfortunately, building and training a machine learning model from scratch is next to impossible due to the lack of hardware infrastructure and finance support. It painfully restricts the requirements of rapid solutions to deal with the demand. For this purpose, the authors have exploited the idea of transfer learning which is the improvement of learning in a new prediction task through the transferability of knowledge from a related prediction task that has already been learned. By utilizing state-of-the-art deep networks re-trained upon our collected data, our extensive experiments show that the proposed combination performs perfectly and achieves the classification accuracy of 98.7% and 98.5% on our collected datasets within the acceptable training time on a normal laptop. A mobile application is also deployed to facilitate further integrated recommendation and services.
Authors and Affiliations
Nghia Duong-Trung, Luyl-Da Quach, Chi-Ngon Nguyen
Keywords
Related Articles
- EP ID EP448674
- DOI 10.14569/IJACSA.2019.0100107
- Views 79
- Downloads 0
Bag of Features Model Using the New Approaches: A Comprehensive Study
The major challenge in content based image retrieval is the semantic gap. Images are described mainly on the basis of their numerical information, while users are more interested in their semantic content and it is reall...
Estimation of Water Quality Parameters Using the Regression Model with Fuzzy K-Means Clustering
The traditional methods in remote sensing used for monitoring and estimating pollutants are generally relied on the spectral response or scattering reflected from water. In this work, a new method has been proposed to fi...
AES-Route Server Model for Location based Services in Road Networks
The now ubiquitous use of Location based services (LBS), within the mobile computing domain, has enabled users to receive accurate points of interest (POI) to their geo-tagged queries. While location-based services provi...
Time Emotional Analysis of Arabic Tweets at Multiple Levels
Sentiment and emotional analyses have recently become effective tools to discover peoples attitudes towards real-life events. While Many corners of the emotional analysis research have been conducted, time emotional anal...
Computation of QoS While Composing Web Services
Composition of web services has emerged as a fast growing field of research since an atomic service in its entirety is not capable to perform a specific task. Composition of web services is a process where a set of web s...