<![CDATA[One of Indonesia's most important commodities, cocoa plants play a crucial role in both the agricultural and financial sectors. In any event, this plant is particularly susceptible to a variety of diseases that can completely reduce its effectiveness, including stem cancer, VSD, natural product spoilage, and others. This article discusses how to use the YOLOv8 technique to develop a framework for sickness conclusion research in cocoa plants. The YOLOv8 computation was selected for this analysis due to its emphasis on handling speed and object recognition with high accuracy. The results showed that the YOLOv8 demonstration could accurately identify damage, reaching a 90% mAP. As a result of these developments, the produced show may be a useful tool to help farmers better monitor and control pest attacks. It is expected that using YOLOv8 to identify damage to cocoa natural products will result in a more effective plan for reducing the likelihood of lower yields from insect attacks.]]>
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