Apples are among the most popular fruits globally due to their health and nutritional benefits for humans. Artificial intelligence in agriculture has advanced, but vision, which improves machine efficiency, speed, and production, still needs to be improved. Managing apple development from planting to harvest affects productivity, quality, and economics. In this study, by establishing a vision system platform with a range of camera types that conforms with orchard standard specifications for data gathering, this work provides two new apple collections: Orchard Fuji Growth Stages (OFGS) and Orchard Apple Varieties (OAV), with preliminary benchmark assessments. Secondly, this research proposes the orchard apple vision transformer method (POA-VT), incorporating novel regularization techniques (CRT) that assist us in boosting efficiency and optimizing the loss functions. The highest accuracy scores are 91.56 % for OFGS and 94.20 % for OAV. Thirdly, an ablation study will be conducted to demonstrate the importance of CRT to the proposed method. Fourthly, the CRT outperforms the baselines by comparing it with the standard regularization functions. Finally, time series analyses predict the ‘Fuji’ growth stage, with the outstanding training and validation RMSE being 19.29 and 19.26, respectively. The proposed method offers high efficiency via multiple tasks and improves the automation of apple systems. It is highly flexible in handling various tasks related to apple fruits. Furthermore, it can integrate with real-time systems, such as UAVs and sorting systems. This research benefits the growth of apple’s robotic vision, development policies, time-sensitive harvesting schedules, and decision-making.

中文翻译：

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基于深度学习的时间序列分类预测苹果品种和生长预测，以影响收获决策


苹果因其对人类的健康和营养益处而成为全球最受欢迎的水果之一。农业中的人工智能已经取得了进步，但提高机器效率、速度和产量的视觉仍需要改进。管理从种植到收获的苹果发展会影响生产力、质量和经济性。在这项研究中，通过建立一个具有一系列符合果园数据收集标准规范的相机类型的视觉系统平台，这项工作提供了两个新的苹果收藏：果园富士生长阶段 （OFGS） 和果园苹果品种 （OAV），并进行了初步基准评估。其次，本研究提出了果园苹果视觉转换器方法 （POA-VT），结合了新颖的正则化技术 （CRT），帮助我们提高效率和优化损失函数。OFGS 的最高准确率得分为 91.56%，OAV 的最高准确率得分为 94.20%。第三，将进行消融研究以证明 CRT 对所提出的方法的重要性。第四，通过与标准正则化函数进行比较，CRT 的性能优于基线。最后，时间序列分析预测了 “Fuji ”生长阶段，出色的训练和验证 RMSE 分别为 19.29 和 19.26。所提出的方法通过多项任务提供高效率，并提高了苹果系统的自动化程度。它在处理与苹果果实相关的各种任务方面具有高度的灵活性。此外，它还可以与实时系统集成，例如无人机和分拣系统。这项研究有利于 Apple 机器人视觉、开发政策、时间敏感的收获计划和决策的发展。