<![CDATA[The agriculture and aquaculture sector has adopted the concept of smart farming based on the Internet of Things (IoT) to monitor and control the cultivation environment with precision. However, the efficiency achieved in the pre-harvest phase is often stopped in the post-harvest phase, especially in yield counting activities. The manual calculation process is highly inefficient and prone to human error, which has a direct impact on logistics management and agribusiness profitability. To overcome these inefficiencies, this study proposes the design and implementation of an automated crop yield counting system that integrates Image Processing and IoT technologies. The system is designed to use cameras and image processing algorithms to accurately detect and calculate the quantity of crops. The calculated data is sent to the IoT cloud platform for real-time storage and access through the Android mobile app. The hardware framework includes microcontrollers such as the ESP32/ESP8266, which have proven reliable in other IoT monitoring systems. The expected result of this study is a prototype system that is able to calculate crop yields with a high level of accuracy and speed that far exceeds manual methods. The Android app implementation provides an intuitive monitoring and control interface, allowing users to access historical and real-time data at any time. The main contribution of this research is to bridge the digitalization gap in the post-harvest phase, as well as provide fast, accurate, and digitized solutions to support comprehensive precision agriculture management.]]>
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