Abstract
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 (R2, 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 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 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.
Highlights
Technological advances have been expanding the modernization of agriculture and, as a result, increasing the productivity and immunity of planted crops
The other studies focused on only one type of cultivation: Goap et al [20] applied their system in soil agriculture, Alipio et al [22] and Sisyanto et al [8] in hydroponics
Activations that the model can perform are: reset the level of the circulation tanks, irrigate at scheduled times, turn on the circulation pump, turn on/off lighting, open/close the shade, turn on/off the hoods, increase or decrease the number of inputs for the plants, turn on the sprinklers, and turn on refrigeration of the nutrient solution. In addition to these suggestions for improvements, IndoorPlant has a prediction module that aims to predict production for the month based on its history of contexts, and to predict alerts before the memos are triggered by the greenhouse control system
Summary
Technological advances have been expanding the modernization of agriculture and, as a result, increasing the productivity and immunity of planted crops. Intelligent agriculture, based on Internet of Things (IoT) technologies, allows farmers to reduce waste and increase productivity, from irrigation with greater precision to the amount of fertilizer used [2]. Species normally cultivated in open fields are increasingly being grown in closed environments such as greenhouses and/or pavilions This change occurs because greenhouses provide the creation of a microclimate more favorable for the species, and better results are obtained in terms of the reduction of pests, less use of pesticides, and greater production, among other aspects. One of the great differentials of Smart Farming is that it seeks to drive new trends such as family farming In this scenario, the IndoorPlant model provides generic intelligent services based on context histories for users. The main objective of this work is to create a computing model for indoor agriculture that uses the historical context of crops and provides intelligent generic services for farmers in different types of cultivation. The last section contains the considerations, conclusion, and future work
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