Conference Agenda

Tomato cultivation is a vital agricultural activity worldwide, contributing significantly to global food production. However, tomato crops are highly susceptible to various diseases, including mold, bacterial spot, and early blight, which can severely impact fruit quality and yield. These diseases, if not detected and managed promptly, lead to increased production costs and decreased efficiency. This research aims to address these challenges by developing and implementing an early disease detection dataset using Convolutional Neural Networks (CNNs). The system was trained with 4,083 images of tomato plants, allowing the CNN model to accurately identify specific diseases in both early and advanced stages. The model achieved a mean Average Precision (mAP) of 86.1%, a precision of 88.2%, and a recall of 82.6%, indicating its effectiveness of the dataset. This dataset can be used to develop different applications for managing tomatoes farm.

The growing demand for quality in the rice industry has driven innovative solutions for classifying rice varieties, preventing mixtures that impact the final product. This study introduces a convolutional neural network (CNN) for automatic rice grain classification using digital images. A dataset of 75,000 images, divided into five categories ('Ipsala,' 'Arborio,' 'Jasmine,' 'Karacadag,' and 'Basmati'), was used. The model was trained with 80% of the data (56,000 images) and validated with the remaining 20% (14,000 images), using 5,000 new images for final evaluation. The CNN achieved 99.2% accuracy, demonstrating high performance even among visually similar varieties. This approach modernizes traditional methods, improving efficiency and ensuring higher-quality products in the Colombian rice industry.

Drying foodstuffs is a widely utilized technique to extend their shelf life, a practice known since ancient times. This process typically relies on heated air as a medium for heat and mass transfer, resulting in significant energy demands. This study investigates the intermittent drying of bananas using a tray dryer under current operational conditions. Although intermittent heating is slower than continuous heating, it effectively mitigates the risks of surface burning or carbonization in dried products. Key parameters such as the psychrometric characteristics of inlet and outlet air, drying duration, and energy consumption were meticulously measured and controlled. The collected data facilitated the plotting of drying curves based on Fick's diffusion law. Upon validation, the findings revealed that intermittent drying is more energy-efficient than continuous drying when both are conducted under similar conditions.

Urban Living Labs (ULLs) serve as dynamic frameworks for fostering socio-technical innovation within smart cities. This study examines the application of a technological innovation management model in the Fenicia ULL, aimed at enhancing the efficiency and sustainability of urban garden management. The project integrates IoT-enabled humidity and temperature sensors, powered by solar energy, to monitor real-time environmental conditions and energy production. These data streams feed into a digital twin platform, enabling predictive analytics and informed decision-making for both community members and urban planners.

This research underscores the alignment between technological advancements and community-driven initiatives, demonstrating how digital twins and renewable energy solutions contribute to participatory governance and urban sustainability. By implementing this innovation management model, the study addresses critical urban challenges, including resource optimization and citizen engagement, particularly in developing regions. The findings provide valuable insights into scalable and replicable models for smart urban agriculture, reinforcing the role of ULLs in shaping resilient and adaptive urban ecosystemss.

This study focuses on analyzing the impact of climate risk on the productivity and financing of the agricultural sector in Honduras. Through exhaustive research, the main factors that explain climate risk and their interaction with Honduran agricultural productivity were identified, analyzing in parallel their subsequent implications in relation to their financing. The main objective of the research involves the analysis of the impact of climate risk on the productivity and financing of the agricultural sector in Honduras, under the scheme of its main products, based on its trade balance and food security; identifying their vulnerabilities and proposing mitigation and adaptation strategies that improve the resilience of the sector to the effects of climate change. The present correlation between climatic variables, precipitation and temperature in the agricultural productivity of corn, coffee, sugar cane and banana crops is exposed, as well as their impact on bank and non-bank financing of the latter; Likewise, the main geographical areas of Honduras with the greatest risk exposure are exposed based on their main productive sectors.