Abstract:
The rubber tree, or Hevea brasiliensis (H. brasiliensis), is one of the most
important commercial crops in the world. It belongs to the family
Euphorbiaceae. The Rubber trees are also a major plantation crop in Sri Lanka.
The Rubber tree is affected by several severe diseases, categorized into rubber
leaf, stem and branch, panel and root diseases. By identifying the real causes
of rubber leaf diseases, the shortcomings of rubber leaf diseases, and the
implications of the existing technology, solutions are provided to identify three
specific diseases that are currently having a great impact on the economy of
Sri Lanka. In most cases, these diseases are diagnosed by humans in Sri Lanka.
When farmers are trying to identify these diseases, it is challenging to identify.
If they make a mistake, the right remedies or treatments will not be able to
give to the infected plant. From this issue, the country will lose a considerable
amount of income. This study offers a machine learning method to
automatically identify the diseases of rubber leaves without the need for
human effort and subjective errors. The Convolutional Neural Network (CNN)
was used to automatically identify and predict rubber leaf diseases. The
experimental results indicated that the CNN model's accuracy is 93%, which
will help more accurately and effectively identify the rubber leaf diseases than
existing methods and technologies. This research provides a solution for
reducing rubber leaf disease diagnosis in Sri Lanka.
