Researchers at the Universidad Internacional Iberoamericana (International Iberoamerican University, UNIB), Dr. Aman Singh and Dr. Ernesto Bautista, propose a system to predict diseases in rice leaves using various deep learning techniques.
Rice is a staple food for half of the world's population and some farmers prefer to grow rice because of its adaptability to different environments. In addition, this food is usually included as a complete food in meals due to its starchy nature and high caloric value. It is an accessible food for everyone.
However, this food is vulnerable to various diseases during the growing process and manual identification of these diseases is inaccurate as farmers do not have medical expertise. Plant diseases have a major impact on rice production and can cause minor damage to the fruit or even the death of the plant. Therefore, it is necessary to look for quick, inexpensive, and accurate ways to identify them.
Thanks to automated methods developed in recent years, it is possible to detect leaf diseases efficiently and quickly. Therefore, in order to contribute to farmers, this research has proposed a model for early identification of rice leaf diseases and thus optimize production. For this purpose, transfer learning algorithms previously trained with various rice leaf disease detection classifiers were used.
Research results
The research took into account the three main rice leaf diseases: BB, BS, and mildew.
According to the research results, the proposed model achieved an accuracy of 91% on a normal data set and 94% on a segmented data set. In the future, it is expected to implement these results to mobile devices in order to automatically recognize diseases in rice leaves and other crops in agriculture.
If you want to know more about this fascinating study, click here.
For further research, check the UNIB repository.
Lastly, the Universidad Internacional Iberoamericana (UNIB) offers the Master in Environmental Management and Audits. This program trains professionals in the environmental area, in the field of consulting, and in the field of research related to the environment.