Conference Agenda

This study developed affordable remote-controlled agrobotic lawnmower powered by a rechargeable battery. It uses linear blade driven by DC motor and the circuit was electronically design using proteus software. An android remote system was designed to control the lawnmower linked to bluetooth application on a mobile device. The wheels, equipped with motor drivers, were programmed to navigate forward, reverse, left, and right. This system was powered by 12V chargeable battery. The cutting blade affixed to motor driver was linked to the battery via a switch. The frame was fabricated using Polyvinyl-Chloride (PVC), rendering it economical and lightweight. It was evaluated and the result shows that the average operating speed for soft and hard grasses are 192 and 179 m/h, average effective field capacity for soft and hard grasses are 73.48 and 53.10m²/h respectively, the average theoretical field capacity for soft and hard grasses are 74.88 and 69.81m²/h, and the average field efficiency on soft and hard grasses are 98% and 76% respectively. The average rate of battery depletion for soft and hard grasses are 1.40 and 1.65 v/h. The lawnmower offers low-income earners an efficient, sustainable alternative to existing models with no harmful gases, operator fatigue, and noise during operation.

The use of Information and Communication Technologies (ICT) is transforming agriculture and natural resources conservation worldwide, and Costa Rica is no exception. This paper analyzes the adoption of ICT in agriculture and environmental conservation in Costa Rica, assessing its contribution to sustainability and sector competitiveness while identifying key challenges and opportunities. The study is framed within the Quintuple Helix model, which emphasizes the interaction among government, industry, academia, civil society, and the natural environment as a driver of sustainable innovation. A mixed-methods approach was applied, combining documentary analysis, bibliometric review, and in-depth interviews with key stakeholders from different sectors. The results show that Costa Rica has made significant progress in the use of ICT for precision agriculture, water management, and environmental monitoring; however, limitations persist, particularly in rural connectivity, technical capacity, and access to financing. Emerging technologies such as the Internet of Things, big data analytics, and digital platforms present strong potential to improve resource efficiency and environmental sustainability. The study concludes that stronger intersectoral collaboration, targeted public policies, and investment in education and infrastructure are essential to accelerate digital transformation in agriculture and natural resources conservation in Costa Rica.

Heat stress severely constrains global potato (Solanum tuberosum L.) production, yet quantifying dynamic physiological responses like radiation use efficiency (RUE) remains methodologically challenging. We conducted a controlled experiment (ambient vs. heat stress; three nitrogen rates: 0, 50, 150 kg ha⁻¹) and developed an integrated vision-modeling framework to dissect heat-nitrogen interactions. To overcome manual sampling limits, a fine-tuned Mask R-CNN segmented multimodal (RGB and thermal) canopy images. RGB-derived canopy cover replaced the traditional fraction of photosynthetically active radiation (fPAR) in a crop model to estimate RUE, while thermal imagery quantified temperature-induced photosynthetic stress. Results revealed that heat stress elevated canopy temperature by 2.7–7.2 °C, reduced RUE by 17.1%, and decreased average tuber yield by 42.1%. However, escalating N application mitigated these penalties: yield loss declined from 55.1% (0 kg ha⁻¹) to 33.3% (150 kg ha⁻¹), driven by sustained canopy retention and stabilized carbon-nitrogen dynamics. By bridging high-throughput multimodal phenotyping with mechanistic modeling, this study elucidates N-mediated heat resilience and provides a scalable framework for climate-smart agriculture.

In recent years, the excessive growth of aquatic plants in lakes and ponds has caused various problems, and water chestnut (Trapa spp.) in particular is regarded as a harmful aquatic weed for which removal is recommended.

The objective of this study was to evaluate the effectiveness of early-season cutting for controlling water chestnut.

Field experiments were conducted at Lake Izunuma, Miyagi Prefecture, Japan. We developed an autonomous unmanned surface vehicle (USV) equipped with a cutter and performed cutting operations in June and July. Four experimental plots were established with different cutting timings and numbers of cutting events. In early September, when biomass tends to reach its seasonal peak, each plot was surveyed using an UAV. The suppression effect was assessed by calculating the surface coverage of water chestnut from the acquired aerial images.

As a result, water chestnut coverage, which was approximately 100% in the uncut control plot, decreased to about 25.1% in the plot cut once and to about 2.5% in the plot cut twice. A similar trend was observed in the same experiment conducted in the previous year, suggesting that early-season cutting can suppress water chestnut growth and that repeated cutting may enhance the suppression effect.

The Asian hornet (Vespa velutina) is an invasive predator of pollinators expanding across Europe. Current nest localization methods rely on manually marking hornets at lure stations, estimating departure direction, and inferring nest distance from return times. Although effective, this procedure is labour-intensive, requires trained operators, and scales poorly. This study explores the feasibility of automating this approach using passive acoustic monitoring. A microphone array installed at a lure station can estimate arrival and departure directions while recording acoustic signatures. These ‘acoustic fingerprints’ may enable automated tracking of visit intervals without manual marking. A pilot experiment was conducted using three lure stations near the Arenberg campus. Each station was equipped with a microphone array, a calibrated reference microphone, and a camera for ground truth validation. Hornets visiting the stations were manually marked and flight times recorded. Results indicate that visit intervals correlate with nest distance (R = 0.96), allowing estimation of flight speed and nest residence time. In one case, hornets from the same nest visited two different lure stations, reducing the nest search area through intersection of estimated distance circles. These results demonstrate the potential of acoustic lure stations as a low-cost, scalable tool for automated Asian hornet nest localization.