ASSESSING PINEAPPLE MATURITY IN COMPLEX SCENARIOS USING AN IMPROVED RETINANET ALGORITHM

ABSTRACT In China, low levels of accuracy in predicting when pineapple crops will reach maturity can result from environmental variation such as light changes, fruit overlap, and shading. Therefore, this study proposed the use of an improved RetinaNet algorithm (ECA-Retinanet) based on the ECA attention mechanism. The ECA attention mechanism was embedded into the classification subnet of RetinaNet to improve accuracy in detecting different levels of maturity in pineapples. A new pineapple dataset was collected comprising four different growth stages under mild and severe complex scenarios. The experimental results have shown that the mAP (Mean Average Precision) and F1 score (Balanced Score) of the ECA-Retinanet model were 97.69%, 94.75%, 93.2%, and 90% for identification in mild and severe complex scenarios. These values are 0.42%, 2%, 1.78%, and 1.5% higher than the original RetinaNet model which exceeds those of the six existing state-of-the-art detection models. The results have indicated that the proposed algorithm could be used for accurate identification of pineapple fruit and can detect fruit maturity using ground color images in the natural environment. The study findings provide a technical reference for automatic picking robots and early yield estimation.

摘要 在中国，受光照变化、果实重叠与遮阴等环境变异因素影响，菠萝成熟期预测准确率偏低的问题较为突出。为此，本研究提出一种基于ECA注意力机制（ECA attention mechanism）的改进型RetinaNet算法（ECA-Retinanet）。将ECA注意力机制嵌入至RetinaNet的分类子网中，以提升菠萝不同成熟等级的检测精度。本研究采集了一套全新的菠萝数据集，涵盖轻度与重度复杂场景下的4个不同生长阶段样本。实验结果显示，在轻度与重度复杂场景的识别任务中，ECA-Retinanet模型的平均精度均值（Mean Average Precision, mAP）分别为97.69%、93.2%，F1分数（Balanced Score）分别为94.75%、90%；上述指标较原始RetinaNet模型分别提升0.42%、2%、1.78%与1.5%，且优于当前6种主流先进检测模型。研究结果证实，所提算法可实现菠萝果实的精准识别，并能基于自然环境下的地面彩色图像完成果实成熟度检测。本研究成果可为自动采摘机器人研发与早期产量预估提供技术参考。