Classification and Detection Rice leaf Diseases Using Information and Communication Technology (ICT) Tools

Abstract

Despite sustainable development, increase in use smartphones, drones, satellite and other information communication technologies for data collection and analyzing for decision marking. Crop losses due to insect pests and diseases are a major threat to farming communities globally. In the case of rice, up to 37% of economic losses are caused by insect pests and disease infestation. Timely and accurate disease and insect pest diagnosis and management can not only reduce crop losses. In the last decade, Information and communication technologies (ICTs) have been increasingly used for information sharing. With mobile internet services becoming available in even the furthest locations, ICT-based agricultural solutions are finding a foothold on the farms of poor smallholders. ICT-based tool that supports diagnosis of insect pests and diseases and enables farmers to make timely decisions for better pest management. To identify the rice diseases at any untimely phases is not yet explored. Early classify and detection for estimation of severity effect or incidence of diseases can save the production from quantitative and qualitative losses, reduce the use of pesticide, and increase country's economic growth. The main challenges is to minimize the impacts of attacks. Detection of plant disease through some automatic technique is beneficial as it requires a large amount of work of monitoring in big farm of crops, and at very early stage itself it detects symptoms of diseases means where they appear on plant leaves. In this paper we review different disease classification techniques that can be used for plant leaf disease detection. Also we describe the data collection by information communications technology for rice leaf, different disease classification approaches that can be used for rice diseases detection. Thirdly we suggested the framework for convolutional neural network in Agricultural sector for detection and identification for innovation technology in agriculture.

Classify Rice Disease Using Self-Optimizing Models and Edge Computing with Agricultural Implications

“…Instead, we combined management strategies for specific rice diseases from known plant experts using edge computing. This was chosen to increase accessibility to the machine learning approach, and allows for our system to meet more farmers where they are and when they need it [16][17][18][19][20].…”