TA DIGITAL
Rancang Bangun Sistem Cerdas Klasifikasi Hama Padi Berbasis Mobile Menggunakan CNN-Based Deep Learning = Mobile-Based Rice Pest Classification Intelligent System Using CNN-Based Deep Learning
Rice is one of the important crops in the economy and as the main food commodity worldwide after wheat and corn. However, rice cultivation often experiences a decrease in crop yields due to diseases and pests that inhibit plant growth. Therefore, rice pest and disease control has always been a priority in the development of the agricultural sector. One of the problems in rice pest control is the detection of the right type of pest so that the handling is more optimal, and the use of pesticides becomes more effective and efficient. In this case, technological developments open up opportunities for optimal pest control with the help of devices. The goal is to utilize machine learning models, to recognize the type of pest and provide recommendations on the handling that should be given. Therefore, a Mobile-based Rice Pest Classification Smart System was built using CNN-Based Deep Learning to make the rice pest control process easier and more effective, with the development of models that have the best performance. The method used in this study is using the waterfall method, where each step of the research is carried out sequentially. It starts with needs analysis or planning, application design, implementation, testing and evaluation or improvement. Testing of mobile-based rice pest classification applications with CNN – Based Deep Learning can run well with a functional satisfaction level of 90% from 10 respondents of the Farmer Group management, and a user satisfaction level of 95% from 20 farmer respondents. The advantage of this application is that it provides a menu to detect rice pests with pictures as well as being able to detect rice pests in real time. So that rice production can be more effective and optimal.
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