Applied Computing Graduate Program, University of Vale do Rio dos Sinos, Av. Unisinos 950, Bairro Cristo Rei, São Leopoldo, RS 93022-750, Brazil.
Electrical Engineering Graduate Program, University of Vale do Rio dos Sinos, Av. Unisinos 950, Bairro Cristo Rei, São Leopoldo, RS 93022-750, Brazil.
Sensors (Basel). 2021 Feb 26;21(5):1631. doi: 10.3390/s21051631.
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.
近年来，无处不在的计算应用日益增多，这主要得益于移动计算、更精确的传感器以及物联网（IoT）特定协议等技术的发展。该研究领域的一个趋势是使用上下文感知。在农业领域，上下文涉及环境，例如温室内部的条件。最近，一系列研究提出了使用传感器来监测生产和/或使用摄像头来获取有关种植的信息，为农民提供数据、提醒和警报。本文提出了一种名为 IndoorPlant 的室内农业计算模型。该模型使用上下文历史记录的分析来提供智能通用服务，例如预测生产力、指示种植可能遇到的问题以及提供温室参数改进建议。IndoorPlant 在三种与水培生产数据相关的农民日常生活场景中进行了测试，这些数据是在 radicchio、生菜和芝麻菜种植七个月期间获得的。最后，本文展示了使用上下文历史记录的智能服务获得的结果。这些场景使用服务来推荐改善种植、档案和最终推荐 radicchio、生菜和芝麻菜的种植时间，使用偏最小二乘（PLS）回归技术。预测结果是相关的，因为得到了以下值：radicchio 的 0.96（R，决定系数）、1.06（RMSEC，校准均方根误差的平方根）和 1.94（RMSECV，交叉验证均方根误差的平方根）；生菜的 0.95（R）、1.37（RMSEC）和 3.31（RMSECV）；芝麻菜的 0.93（R）、1.10（RMSEC）和 1.89（RMSECV）。八位在农场具有不同功能的农民根据技术接受模型（TAM）填写了一份调查。结果显示，在效用方面有 92%的接受率，在易用性方面有 98%的接受率。
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