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When OneWireless Technology is Not Enough: A Network Architecture for Precision Agriculture Using LoRa, Wi-Fi, and LTE
(2022-01-03) Brenes Carranza, José Antonio; Marín Raventós, Gabriela
The world population will reach nearly 10 billion people by 2050, according to the United Nations. Therefore, more food to supply the world’s demand will be required in the following years. Precision agriculture emerges as an option to satisfy the growing demand. In smart farming, wireless sensor networks (WSNs) are crucial in the deployment of sensors in crop fields. Precision agriculture includes crop monitoring and fertigation control. Monitoring and control have distinct network requirements. While monitoring stations deployment requires long-range networks, control stations have other requirements like low latency. For that reason, the use of a combination of WSN is necessary. In this paper, we present an option of network architecture for precision agriculture projects. The architecture includes the use of
LoRa for monitoring stations and Wi-Fi/LTE for control stations. Currently, we are working on smart fertigation in greenhouses. For the architecture, we consider the typical requirements for smart farming projects, but also our project’s requirements.
Early Detection of Diseases in Precision Agriculture Processes Supported by Technology
(2021-03-07) Brenes Carranza, José Antonio; Eger, Markus; Marín Raventós, Gabriela
One of the biggest challenges for farmers is the prevention of disease appearance on crops. Farmers must deal with many different diseases, varying according to each crop produced. Governments around the world have specialized offices in charge of controlling border product entry to reduce the number of diseases af-fecting local producers. Even though governments and producers work together to fight against disease appearance and propagation, it is important to reduce the spread of diseases as quickly as possible in crop fields. For this reason, it is cru-cial to detect diseases in the early stages of propagation, to enable farmers to at-tack them on time, or remove the affected plants. In this research, we propose to use convolution 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.
A neural network approach for real-time monitoring of cannabis sativa L. germination
(2025-02) Brenes Carranza, José Antonio; Codes Alcaraz, Ana Maria; Rocamora Osorio, Carmen; Ferrández Pastor, Francisco Javier
One of the primary challenges in detecting crop germination in seedbeds using computer vision is the difficulty of accurately analysing cases of overlapping seedlings. This study investigates the effectiveness of Convolutional Neural Network (CNN) models and Long Short-Term Memory (LSTM) models in addressing this issue. Utilizing a substantial dataset comprising over 80,000 labelled images of Cannabis Sativa plants, our research aims to compare the performance of standalone CNN models with hybrid architectures (CSS+LSTM model). A noteworthy aspect of our experimental methodology is the deliberate omission of data augmentation techniques during dataset preparation. This decision enables us to evaluate the inherent quality and utility of our curated dataset without introducing artificial modifications. Furthermore, recognizing the significance of incorporating a temporal component in germination detection, we conduct a specific assessment of the hybrid model (CNN+LSTM) against the standalone CNN model. Through comprehensive experimentation and analysis, we evaluate the relative effectiveness of each model in accurately classifying germination status across different levels of seedling overlap. Preliminary results reflect a better performance of the hybrid model compared to the standalone CNN model. However, it is
crucial to consider the computational resources, time, and effort required for training and testing the model. Thus, our study provides valuable insights into the intricate interplay among model architecture, dataset characteristics, and the complexity of the germination detection task. Moreover, this research contributes to the advancement of practical applications of deep learning methodologies in agricultural monitoring, emphasizing the necessity of tailored model designs to overcome the unique challenges encountered in greenhouse environments.
A comprehensive dataset for monitoring germination of Cannabis sativa in greenhouse-controlled environments
(2025-02) Brenes Carranza, José Antonio; Codes Alcaraz, Ana Maria; Ferrández Pastor, Francisco Javier; Rocamora Osorio, Carmen
Germination monitoring in crucial: it aids in planning and maximizing crop yields, contributing to sustainable agriculture practices. Furthermore, in the cultivation of Cannabis sativa L., precise control over the number of plants during the germination process is critical due to legal and regulatory restrictions. To assist growers in tracking germination progress, we planned to developed a classifier that informs them precisely when a plant has successfully germinated in the seedbed. A well-curated dataset is essential for
teaching the algorithm to recognize different features. The dataset should encompass a diverse range of examples to ensure that the classifier can accurately identify and categorize instances it encounters during its operation. The quality and diversity of the dataset play a pivotal role in the performance and reliability of the developed classifier. Due to the limited information available on cannabis crops, we undertook the task of constructing an image dataset from the ground up. This dataset was meticulously crafted through a rigorous process. To build the dataset, we sow two varieties of cannabis, Finola and Kompolti, in separate seed trays, each containing 72 cells, in a greenhouse. A camera fixed above the seedbeds took one image every hour throughout the germination experiment. Four iterations were carried out: two without controlling climatic conditions, and two with controlled conditions and photoperiod. Then, we cropped the images and applied a homography process to correct perspective. The resulting images of each cell for each date and hour were labelled using six categories. first, the images were labelled considering the categories germination, nongermination, only cotyledon, and true leaves, then, they were labelled again using the categories invasion and non-invasion. The result was a comprehensive dataset comprising 80,640 images of seedbed cells showcasing plants at various growth stages. This paper outlines the step-by-step process employed in creating this image dataset.
Evaluation of Usability, User Experience and Accessibility of Applications: a Tertiary Review
(2024-11-12) Piedra Pacheco, Antonio Jesús; López Herrera, Gustavo; Brenes Carranza, José Antonio; Valverde, Joseph; Díaz Oreiro, Ignacio
In the present time, there is a growing need to understand how users interact with digital applications to ensure the success of new products and a good experience for the users. Three main concepts are central to this subject: usability, user experience, and accessibility. As such research efforts have been undertaken to create evaluation instruments for each and apply them to multiple tools. This study aims to perform a compilation of the research gathered in multiple secondary studies on the subject of evaluating these concepts. A systematic search process was performed to answer research questions related to how evaluations in the field of usability, user experience, and accessibility are performed. From this search 45 secondary studies were obtained and further analyzed. We were able to identify the most common methodologies and tools highlighted across the secondary studies for each concept. The main results found were the following: questionnaires are ubiquitous in the field of user experience evaluation and to a lesser extent for usability while accessibility evaluation is mostly performed with the aid of automated tools, there is a tendency for authors to develop and use their own questionnaires without validating them and 24 main categories for the evaluated characteristics were found, of which Satisfaction, Efficiency, Effectiveness, and Attractiveness were the most common.