人工智能辅助种植策略对温室草莓生产调控 效果对比研究. (Chinese)

Artificial intelligence (AI) assisted planting can improve in the precise management of protected horticultural crops while also alleviating the increasingly prevalent problem of labor shortage. As a typical representative of labor-intensive indus‐ tries, the strawberry industry has a growing need for intelligent technology. To assess the regulatory effects of various AI strate‐ gies and key technologies on strawberry production in greenhouse, as well as provide valuable references for the innovation and industrial application of AI in horticultural crops, four AI planting strategies were evaluated. Four 96 m2 modern greenhouses were used for planting strawberry plants. Each greenhouse was equipped with standard sensors and actuators, and growers used artificial intelligence algorithms to remotely control the greenhouse climate and crop growth. The regulatory effects of four dif‐ ferent AI planting strategies on strawberry growth, fruit yield and qualitywere compared and analyzed. And human-operated cul‐ tivation was taken as a reference to analyze the characteristics, existing problems and shortages. Each AI planting strategy simu‐ lated and forecast the greenhouse environment and crop growth by constructing models. AI-1 implemented greenhouse manage‐ ment decisions primarily through the knowledge graph method, whereas AI-2 transferred the intelligent planting model of Dutch greenhouse tomato planting to strawberry planting. AI-3 and AI-4 created growth and development models for strawber‐ ries based on World Food Studies (WOFOST) and Product of Thermal Effectiveness and Photosynthesis Active Radiation (TEP), respectively. The results showed that all AI supported strategy outperformed a human-operated greenhouse that served as reference. In comparison to the human-operated cultivation group, the average yield and output value of the AI planting strate‐ gy group increased 1.66 and 1.82 times, respectively, while the highest Return on Investment increased 1.27 times. AI can effec‐ tively improve the accuracy of strawberry planting management and regulation, reduce water, fertilizer, labor input, and obtain higher returns under greenhouse production conditions equipped with relatively complete intelligent equipment and control com‐ ponents, all with the goal of high yield and quality. Key technologies such as knowledge graphs, deep learning, visual recogni‐ tion, crop models, and crop growth simulators all played a unique role in strawberry AI planting. The average yield and Return on Investment (ROI) of the AI groups were greater than those of the human-operated cultivation group. More specifically, the regulation of AI-1 on crop development and production was relatively stable, integrating expert experience, crop data, and envi‐ ronmental data with knowledge graphs to create a standardized strawberry planting knowledge structure as well as intelligent planting decision-making approach. In this study, AI-1 achieved the highest yield, the heaviest average fruit weight, and the highest ROI. This group's AI-assisted strategy optimized the regulatory effect of growth, development, and yield formation of strawberry crops in consideration of high yield and quality. However, there are still issues to be resolved, such as the difficulty of simulating the disturbance caused by manual management and collecting crop ontology data. [ABSTRACT FROM AUTHOR]

人工智能 （Artificial Intelligence，AI） 辅助种植有助于提高设施园艺作物精准化管理水平、缓解日 益凸显的劳动力紧缺问题。草莓是典型的劳动密集型园艺作物，研究对比采用不同AI种植策略和关键技术 对草莓温室生产的调控效果，可对园艺作物种植的AI技术改进和产业化应用提供参考。本研究对比分析了4 个不同AI种植策略对草莓生长发育和产量及品质的调控效果，并以人工种植管理为参照，对AI种植的技术 特点和存在问题进行了分析。结果表明，知识图谱、深度学习、视觉识别、作物模型和作物生长仿真器等 技术在草莓AI种植中各有优势。其中，AI-1组采用知识图谱技术将专家经验、作物数据和环境数据进行融 合，建立了标准化草莓种植知识结构和智慧种植决策方法，对作物生产发育的调控较为稳健，以较低的投 入获得了最高产值。与人工种植管理相比，AI种植策略组的平均产量提高了 1.66倍，平均产值提高了 1.82 倍，最高投入产投比提高了 1.27倍。针对高产优质的目标，在配备较完善的智能化设备和控制组件的温室 生产条件下，AI辅助种植能有效提高草莓种植管控的精准度，减少水肥和劳动力的投入，获得较高的收益， 但也存在对人工管理扰动的模拟难、作物本体信息采集难等问题. [ABSTRACT FROM AUTHOR]

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