Development of Automatic Marigold Leaf Disease Diagnosis System Using IoT Technology for Support Smart Farmer

Abstract

The study aims to 1) to develop an algorithm for diagnosing marigold leaf disease with IoT technology and 2) to increase the efficiency in the determination of leaf spot disease with IoT technology. This is a measurement and an evaluation of plant disease outbreak to prevent crop decline using photographic data. Support Vector Machine technique is used to help determining marigold leaf disease, Alternaria sp. The efficiency test found that SVM model yielded a result of 86 percent. In addition, the satisfaction assessment results showed that farmers had the highest level of satisfaction with the diagnostic technique. At the mean level was 4.46 and the standard deviation was 0.53. The results of the diagnostic efficiency test before and after the use of the IoT system found that there were significant differences. Therefore, the technique of diagnosing marigold leaf disease with IoT technology enables farmers to diagnose leaf disease more effective.