<![CDATA[Potato (Solanum tuberosum L.) is one of major food crops with high economic value in Indonesia. But, the productivity of cardamom is generally constrained due to the attack of pests and diseases which results in quantitative as well as qualitative losses. Often, farmers are not able to quickly and accurately diagnose pests or diseases, thereby delaying control measures. The objective of this study is to create an Android application for the identification and classification of pests and diseases in potatoes, based on Teachable Machine's capabilities. The mobile phone-based application records the leaf images of potato by using a smartphone camera and processes them with a classification model, returning detection results and providing the control strategy. Their development lifecycle follows the Waterfall model, which includes communication, planning, design, construction and implementation stages. Experiments were conducted to compare the detection rate, response time, and ease of use. The results show that the application can recognize several types of pests and diseases with satisfactory accuracy and responsiveness, providing a practical tool for farmers to perform early detection and apply appropriate control measures]]>
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