"Manual assessment of oil palm fresh fruit bunch (FFB) ripeness in plantations is often inconsistent, time-consuming, and heavily reliant on individual experience. These inefficiencies can lead to mistimed harvesting, reduced oil quality, and lower yields. HarvestSmart addresses this challenge by providing a mobile-based, AI-powered solution for real-time ripeness detection and daily harvest reporting. The system leverages a lightweight deep learning model based on YOLOv10, enhanced with Low-Rank Adaptation (LoRA) to significantly reduce the number of trainable parameters and computational load. The model was optimized and converted to TensorFlow Lite for seamless execution on Android devices, even in offline rural environments. Farmers can use the app to scan oil palm bunches in the field, receive immediate classification results—such as ripe, underripe, overripe, or abnormal—and generate PDF-based harvest summaries. In addition to detection, the app features a reporting module that archives daily results, helping farmers and supervisors monitor harvest quality and make data-driven decisions over time. The interface is built with simplicity in mind, ensuring ease of use for non-technical users. By integrating AI into everyday farming workflows, HarvestSmart improves productivity, minimizes waste, and empowers smallholder farmers with accessible agricultural technology—turning smartphones into powerful harvest tools."