Tipo de publicación: Conference Paper

Publicado en: Advances in Human Factors and Systems Interaction

Descripción:

Smart agriculture is an active research field. Currently, many researchers are working on the construction of platforms directed to improve efficiency, crop processes, and data awareness. However, it is common that developers focus on data monitoring instead of the data relevance for decision making or the costs associated with the creation of monitoring platforms. In this paper, we present a classification of the data required by researchers on the construction of decision-support systems applied to smart agriculture processes. By using this classification, the user can define which data are relevant according to the characteristics of the problem that needs to be solved.We have applied the classification to data recollected in a study case conducted by the end of last year. Besides, we identify a list of agronomic and climatic variables commonly used in the construction of decision support systems. We apply the classification to this list of variables as an example for researchers. As a conclusion, this typification permits the researcher to identify data that has to be monitored and controlled, and data that does not have to be measured, the later based on the data characteristics and utility for the farmer.

Tipo de publicación: Conference Paper

Publicado en: 2019 IV Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)

Descripción:

One of the biggest challenges for farmers is the prevention of disease appearance on crops. Governments around the world control border product entry to reduce the number of foreign diseases affecting local producers. Evenmore, it is also important to reduce the spread of crop diseases as quickly as possible and in early stages of propagation, to enable farmers to attack them on time, or to remove the affected plants. In this research, we propose the use of convolutional neural networks to detect diseases in horticultural crops. We compare the results of disease classification in images of plant leaves, in terms of performance, time execution, and classifier size. In the analysis, we implement two distinct classifiers, a densenet-161 pre-trained model and a custom created model. We concluded that for disease detection in tomato crops, our custom model has better execution time and size, and the classification performance is acceptable. Therefore, the custom model could be useful to use to create a solution that helps small farmers in rural areas in resource-limited mobile devices.

Tipo de publicación: Book Chapter

Publicado en: Advances in Sustainability Science and Technology

Descripción:

Smart farming solutions seek to help farmers in their daily activities. Their use has shown that it is beneficial for farmers to be aware of the distinct variables affecting the production. For this reason, having alerts and notifications in monitoring and control platforms is crucial. However, in some circumstances, farmers cannot attend to the messages delivered through traditional mechanisms, making it impossible for them to be informed at the right moment. In this paper, we present the design evaluation of an intelligent context-aware smart notifications system for precision agriculture. We consider using distinct notification mechanisms to improve the delivery of notifications to the farmers. We carry out an anticipated user experience evaluation to assess the system’s design and validate the use of the notification mechanisms in distinct scenarios. A total of 48 potential users from Spain and Costa Rica participated in the evaluation. The results show that our proposed system can be very helpful in supporting farmers to be aware of the state of crops. In addition, non-traditional notification mechanisms can potentially keep the farmers informed without affecting their daily activities. Costa Rican potential users value the system’s novelty more than Spanish users.

Tipo de publicación: Conference Paper

Publicado en: Proceedings of the International Conference on Ubiquitous Computing Ambient Intelligence (UCAmI 2022)

Descripción:

In the dynamic landscape of modern agriculture, integrating technology holds immense potential to enhance efficiency and productivity for small-scale farmers. This study presents a user-centric evaluation of an intelligent context-aware alert system, tailored for small-scale greenhouse farming. We employed standardized questionnaires, including the NASA Task Load Index and the User Experience Questionnaire, to assess the system's perceived utility, mental workload, and overall user experience. Our findings reveal the high perceived utility of the system among farmers. Farmers participating in the assessment indicated a strong intention to utilize the system for crop monitoring. Moreover, the system demonstrated a moderate mental workload, suggesting ease of use and potential acceptance by users. Our evaluation highlighted an excellent user experience, with scores ranging from very good to extremely good across all dimensions. Furthermore, user preferences for alert mechanisms underscored the importance of adaptable notifications, with voice and text alerts favored for comprehensive information dissemination. Light and voice alerts were preferred during manual tasks. This study highlights the significance of user-centered design in agricultural technology, offering insights to enhance the usability and the adoption of alert systems in small-scale farming environments. The positive reception of the system's utility and the moderate mental workload suggest that such technology can be readily adopted by farmers, thereby improving monitoring and management practices in greenhouse farming. The preference for adaptable alert mechanisms further emphasizes the need for flexible and context-sensitive solutions in agricultural technology.

Tipo de publicación: Journal Article

Publicado en: Front. Comput. Sci.

Descripción:

In the near years, the world population will have to deal with a big problem: to pro- duce the food required to satisfy its increasing demand. It is imperative to support farmers to find a solution. In this regard, the information and communication technologies (ICT) sector can help by creating technology which supports agriculture processes to increase the crop production and to reduce the resources consumption. In this paper, we present a cost-efficient solution for small scale farmers. Our platform helps the farmers in the decision-making process by using data recollected from crops and by controlling fertigation. Using it, farmers can be more efficient and waste fewer re- sources.

Tipo de publicación: Conference Proceedings

Descripción:

In the context of smart agriculture, developing deep learning models demands large and high- quality datasets for training. However, the current lack of such datasets for specific crops poses a significant challenge to the progress of this field. This research proposes an automated method to facilitate the creation of training datasets through automated image capture and pre-processing. The method’s efficacy is demonstrated through two study cases conducted in a Cannabis Sativa cultivation setting. By leveraging automated processes, the proposed approach enables to create large-volume and high-quality datasets, significantly reducing human effort. The results indicate that the proposed method not only simplifies dataset creation but also allows researchers to concentrate on other critical tasks, such as refining image labeling and advancing artificial intelligence model creation. This work contributes towards efficient and accurate deep learning applications in smart agriculture.

Tipo de publicación: Journal Article

Publicado en: Int. J. Cybern. Inform.