Constituting the agriculture solid substance manufacture, the post-harvest sector processing schema is direct to preventing reduce the losses in intelligent agriculture. Many processing schemata will be preventing post-harvest losses on the agriculture solid substance manufacture, especially sekai-ichi apple is the regularly used fruit also used to make active in human-related activities of the sensory and control function consisting of an agricultural industry. Sekai-ichi apple is being a definite number of diseases induce, but it is to the highest degree of wastage involving in the Post-Harvest process. Especially sekai-ichi apple count loss is an unsafe many time because it not critically post-harvest. Regardless of consideration, the existing hierarchical model specified post-harvest losses prevention research has deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. This paper suggests a “Hierarchical Model within Ontology Enabled IoT” for distinguishable healthy separation of sekai-ichi apple by using Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE). Sekai-ichi apple count loss is always safe on critically post-harvest. Proposed Post-Harvest hierarchical model specified post-harvest losses prevention and deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. In these suggestions, the separation cognitive operation takes the three levels of processing schemes such as lower level, middle level, and higher level. Firstly, the lower level is express agreements with the dynamic functioning for maintaining the definite number of manual induces. This lower level showing an absorption with the activity of manual separation by the human reliability determination. Secondly, the middle level is an express arrangement with the dynamic functioning for reducing the overfitting and accommodate to fitting the right shape deliberation. Middle level is establishing being generalized by concentrating the time-varying features in the occurrence of a change for the worse identification. Finally, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation connection. This interpretability process helps to make the proven position of a prominent classification in a particular fruit on the agriculture solid substance. These three processing flow constructs the ontology structure with manually collected sekai-ichi apple images from a 3D sensor. The observational consequences express that the proposed BCNAE framework recognizes a detection performance carrying out with an optimized—separation ratio for time-variant of the separation process.

构成农业固体物质生产的收获后处理方案直接防止了智能农业的损失。许多加工方案将防止收获后对农业固体物质的生产造成的损失，特别是sekai-ichi苹果是经常使用的水果，也用于使人类活动相关的感官和控制功能活跃于农业，构成农业。Sekai-ichi苹果是一定数量的诱发疾病，但它在收获后过程中的浪费程度最高。尤其是sekai-ichi苹果计数损失在很多时候都是不安全的，因为它并不是至关重要的收获后。不管考虑 现有的针对收获后损失预防研究的层次模型缺乏精确，快速地检测浪费的能力，从而无法确保农业环境的健康分离。本文提出了一种使用增强连续非空间整体属性提取（BCNAE）来区分sekai-ichi苹果健康分离的“基于本体的IoT中的分层模型”。在收获后的关键时刻，Sekai-ichi苹果数丢失始终是安全的。拟议的收获后等级模型规定了收获后损失的预防和不足，无法准确，快速地检测浪费，以确保健康隔离农业环境。在这些建议中，分离认知操作采用了三个处理方案级别，例如低级，中级和高级。首先，较低的级别是具有动态功能的明确协议，用于维持一定数量的手动诱导。该较低水平显示出通过人工可靠性确定具有手动分离活性的吸收。其次，中间层是具有动态功能的快速布置，用于减少过拟合并适应于合适的形状考虑。通过集中在发生变化时的时变特征来识别更差，从而建立起中层的概括。最后，借助seichi-ichi苹果图像分割连接功能来表达特征的上层表达。这种可解释性的过程有助于在农业固体物质上的特定水果中确立突出的分类标准。这三个处理流程使用从3D传感器手动收集的seichi-ichi苹果图像来构建本体结构。观察结果表明，提出的BCNAE框架认可了针对分离过程随时间变化的优化分离比所实现的检测性能。