The environment configuration for the experiment.

Apples are one of the most productive fruits in the world, in addition to their nutritional and health advantages for humans. Even with the continuous development of AI in agriculture in general and apples in particular, automated systems continue to encounter challenges identifying rotten fruit and variations within the same apple category, as well as similarity in type, color, and shape of different fruit varieties. These issues, in addition to apple diseases, substantially impact the economy, productivity, and marketing quality. In this paper, we first provide a novel comprehensive collection named Apple Fruit Varieties Collection (AFVC) with 29,750 images through 85 classes. Second, we distinguish fresh and rotten apples with Apple Fruit Quality Categorization (AFQC), which has 2,320 photos. Third, an Apple Diseases Extensive Collection (ADEC), comprised of 2,976 images with seven classes, was offered. Fourth, following the state of the art, we develop an Optimized Apple Orchard Model (OAOM) with a new loss function named measured focal cross-entropy (MFCE), which assists in improving the proposed model’s efficiency. The proposed OAOM gives the highest performance for apple varieties identification with AFVC; accuracy was 93.85%. For the apples rotten recognition with AFQC, accuracy was 98.28%. For the identification of the diseases via ADEC, it was 99.66%. OAOM works with high efficiency and outperforms the baselines. The suggested technique boosts apple system automation with numerous duties and outstanding effectiveness. This research benefits the growth of apple’s robotic vision, development policies, automatic sorting systems, and decision-making enhancement.

苹果是全球产量最高的果品之一，同时兼具对人体有益的营养与健康价值。尽管人工智能（AI）在农业领域整体发展、且在苹果产业方向取得持续进展，但自动化识别系统仍面临诸多挑战：难以精准识别腐烂果实、同一苹果品类内的个体差异，以及不同果品品种间在类型、色泽与外形上的高度相似性。上述问题，加之苹果病害问题，均会对产业经济、生产效率与商品品质造成严重负面影响。本研究首先构建了一款全新的综合性数据集——苹果品种数据集（Apple Fruit Varieties Collection, AFVC），涵盖85个类别共29750张图像。其次，构建了苹果品质分类数据集（Apple Fruit Quality Categorization, AFQC），包含2320张图像，用于区分新鲜苹果与腐烂苹果。第三，发布了苹果病害全景数据集（Apple Diseases Extensive Collection, ADEC），涵盖7个类别共2976张图像。第四，基于当前前沿研究进展，本研究提出了优化苹果园模型（Optimized Apple Orchard Model, OAOM），并设计了一种名为实测焦点交叉熵（measured focal cross-entropy, MFCE）的新型损失函数，以提升所提模型的运算效率。在基于AFVC的苹果品种识别任务中，所提OAOM取得了最优性能，准确率达93.85%；在基于AFQC的苹果腐烂识别任务中，准确率为98.28%；而在基于ADEC的苹果病害识别任务中，准确率达到99.66%。OAOM具备高效的运算性能，且优于各类基准模型。本研究提出的技术方案可有效提升苹果产业自动化系统的多任务处理能力与综合效能。本研究成果可为苹果产业机器人视觉技术发展、产业政策制定、自动分选系统优化以及决策水平提升提供有力支撑。