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.

The applications of ubiquitous computing have increased in recent years, especially due to the development of technologies such as mobile computing…

Characterized by crop cultivation in closed environments, indoor agriculture impacts not only food production but also environmental sustainability, resource optimization, and food security. Innovative strategies, such as the application of machine learning, play a strategic role in addressing the complexities of indoor farming. The analysis of integrated diverse data sources through machine learning (such as temperature, humidity, light, and soil conditions) enables a comprehensive understanding of plant behavior, growth patterns, and system performance. These technologies improve the analysis of agricultural environments, allowing more precise and efficient crop management. Therefore, this article presents a systematic literature review of works related to the application of machine learning in Indoor Agriculture. This research paper analyzes studies published across eight different databases up to the end of 2025. The initial research resulted in over fourteen thousand articles. After applying the inclusion and exclusion criteria, those were reduced to one hundred and fourteen studies selected for full analysis, which led to the selection of seventy final articles to answer the eleven questions proposed in this research paper. This work presents a taxonomy for mapping explored services in indoor agriculture and another for organizing machine learning techniques applied in indoor agriculture research. The results reveal that the selected articles employed sixty-two machine-learning techniques. The analysis identified twenty-seven types of information collected from cultivations, the use of twenty-five different plant species, five types of data collection devices, and five cultivation systems.

O presente artigo descreve um mapeamento sistemático dos trabalhos relacionados à aplicação de Aprendizado de Máquina na Agricultura Indoor. Esta pesquisa abrange buscas realizadas até março de 2024 nas bases IEEE Xplore, ACMDigital Library, Springer Library, Science Direct, Scopus, MDPI,Wiley e Taylor & Francis. A busca inicial resultou em10.149 artigos, dos quais foram selecionados 76 estudos para leitura completa após a aplicação dos critérios de inclusão e exclusão. Essa análise resultou na seleção de 36 artigos que foram estudados como intuito de responder às 9 questões depesquisa juntamente comuma proposta de taxonomia. Os principais resultados revelam que os artigos selecionados empregaram 43 técnicas diferentes de aprendizado de máquina. Além disso, constatou-se que nenhum estudo foi publicado antes do ano de 2018. Foram identificadas 26 informações distintas coletadas dos cultivos, observou-se o cultivo de 17 culturas diferentes e o emprego de 4 dispositivos para coleta das informações, entre outras informaçõesrelevantes. A análise dos artigos evidenciou uma clara tendência no uso de Aprendizado de Máquina na Agricultura Indoor.