ADA-Net: A Lightweight Model for Apple Flower Maturity Detection in Horticultural Plant Monitoring

Abstract

In modern orchards, the pollination process of apple blossoms plays a crucial role in determining both the quality and yield of the fruit. While most current studies concentrate on identifying individual apple flowers, there is limited research on assessing the developmental stages of apple flowers in dynamic and complex orchard settings. Challenges arise due to the intricate environmental factors and subtle color changes in the anthers following the maturation of the apple flowers, which complicate accurate detection. To overcome these challenges, ADA-Net, an efficient YOLOv8n-based detection model, is proposed to evaluate the maturity stages of apple flowers. First, the adaptive downsampling network module replaces the conventional downsampling convolution, which reduces the size of the convolutional kernels and groups input feature mean average precision (maps). This modification helps reduce the model’s parameter count and computational complexity, while simultaneously improving detection of small targets. In addition, inspired by the task alignment technique of the task-aligned one-stage object detection (TOOD) model, a DAD Head is employed to separate the classification from localization tasks, thus minimizing task interference and improving overall accuracy. A custom apple flower dataset is used to test the model, and the results show detection accuracies of 80.7% for mature flowers and 82.4% for immature flowers, with a total model parameter count of just 1.8 million. These results offer important insights for advancing the development of automated pollination systems in orchards.

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