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In modern precision agronomy, Crop Registration  remains the industry standard for monitoring plant health. However, traditional manual sampling is typically limited to a representative subset (<0.1% of total population). This heuristic approach is inherently prone to sampling bias and fails to capture the high-degree spatial variability of greenhouse microclimates. The FRAVEBOT platform shifts the paradigm from sampling to census-scale data acquisition . Our objective is to

Installed in February 2025 in a tomato crop, the FRAVEye device at the GreenCoop company in Slovakia represents our first and longest-operating unit in a greenhouse environment. At the time of writing, it has captured over 4224  detections, 2637  of them belonging to the monitored species, detecting both pest and beneficial insects. FRAVEye unit installed in the tomato compartment at GreenCoop, Slovakia Since the early start of the season, FRAVEye has been collecting informat

Everyone in AgTech talks about automation, but they often forget the most important part: power. A robot with a dead battery is just an expensive obstacle. Running a fleet of autonomous robots 24/7 in a large-scale greenhouse presents a massive power challenge. Last season, we learned this the hard way. Our old charging strategy was a constant bottleneck. It was manual, inefficient, and created logistical headaches that held back our "automated" system. For this season, we en

FRAVEBOT Scout is now part of the HORTIVEC innovation project at PSKW in Belgium. The collaboration focuses on developing advanced AI models for greenhouse pest and disease detection and validating next-generation navigation technology under real research-station conditions. Since March , the FRAVEBOT Scout has been operating full-time at the PSKW research centre in Sint-Katelijne-Waver. Over this period we have been steadily improving our AI models for recognition of pests,

Modern greenhouses are complex, dynamic spaces. FRAVEBOT’s autonomous navigation stack integrates LiDAR, SLAM, RTK-GPS, and AI vision into a unified safety-first system. The result: sub-3 cm accuracy  and a navigation platform ready for cooperative operation with other machines and human workers. How do you navigate a robot in a modern greenhouse covering several hectares — all while working alongside dozens of other machines and human operators? Challenge accepted. Developin

Following successful deployments in tomato and cucumber production, FRAVEBOT has now expanded its multi-crop capabilities  to include bell peppers . During a one-month pilot with an innovative Dutch producer, we validated the Scout’s performance in a new greenhouse environment and collected valuable data for AI model development. Implementation of the New Crop The first deployment of the FRAVEBOT Scout  in pepper production took place in September. The installation process —

FRAVEBOT’s AI-driven fruit counting has achieved a new milestone. During a four-month trial with a Dutch tomato producer, the Scout reached 98.8% accuracy in detecting and counting beef tomatoes. This success proves the robustness of our training pipeline, enabling new crop segments to be calibrated and deployed within just three weeks. We are proud to announce a major milestone in our fruit counting and ripeness assessment technology. During a four-month trial with a leading

Originally designed for whitefly and thrips monitoring in tomato and cucumber crops, FRAVEye is now expanding its applications into flower production and other protected cultivation systems. With new adaptations for humid environments, increased shading, and an extended list of monitored pest and beneficial species, FRAVEye continues to evolve into a versatile solution for greenhouse pest management and IPM support. From High-Wire Crops to New Greenhouse Systems FRAVEye was d