| Summary: | Pineapple diseases are linked to worms, viruses, bacteria, and fungi. Insects including ants, scales, mealybugs, and souring beetles are some of the most prevalent pests that can harm the pineapple industry over time if early pineapple leaf disease detection is not made There are many smallholders especially in the rural area who lack of latest technology, knowledge, and experience about pineapple disease treatment and management. Thus, lack of a precise diagnosis method prevents them from understanding the disease and creates an effective control for its spread. This issue further impacts the effectiveness of pineapple production, subsequently affecting their income and the country's, as well as jeopardizing national food security. This study presents an innovative approach aimed at enhancing early detection and management of pineapple leaf diseases by integrating a Convolutional Neural Network (CNN) algorithm and import it using TensorFlow Lite into an android mobile. The CNN model achieved an impressive total accuracy of 98% in precisely classifying three types of pineapple leaf diseases: Leaf Spot, Mealybug Wilt, and Pink Disease. When implemented in the mobile application, the system attained an overall confidence of 83.33% by leveraging both camera-captured and gallery images. Although the accuracy gained is impressive, additional research and adjustments can be conducted to improve the system's performance and achieve even higher levels of accuracy. © 2024 American Institute of Physics Inc.. All rights reserved.
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