Real-time banana harvest readiness prediction using mobile SE-enhanced YOLO classification

A digital banana harvesting solution was developed to improve the speed and consistency of banana harvesting by integrating real-time bunch detection with harvest-readiness classification into a mobile decision support system used directly in the field. The banana bunch detection module utilizes a You Only Look Once (YOLO) model trained on a custom dataset collected under real plantation conditions, enabling consistent performance across varied environments. Specifically, a YOLOv12n detector was used for banana bunch detection, achieving 93% AP50-test with an inference latency of 5.1 ms per image, making it suitable for mobile deployment in plantation environments. For the readiness of harvesting prediction, a second model was developed, based on a squeeze-and-excitation YOLO classifier, using annotated images gathered with guidance from harvesting experts. In this work, this SE-enhanced YOLO classifier is used as a lightweight, task-specific YOLO classification backbone for the binary “cut” vs “keep” decision, and this harvest-readiness classifier achieved 94% accuracy with an inference time of 2.8 ms per image. Then, an application was built using Flutter and Dart, which uses intuitive interfaces for both field operators and administrators, and includes integrated feedback mechanisms to collect user input and support continuous model refinement. Field testing across diverse lighting and environmental conditions, as well as usability assessments with expert harvesters and administrative staff, demonstrated reliable performance with potential to contribute to faster decision-making and reduced manual labour.