Computer Vision-Based Monitoring and Data Integration in a Multi-Trophic Controlled-Environment Agriculture Demonstrator

Abstract

Controlled-environment agriculture (CEA) and circular production systems require coordinated monitoring of biological and physicochemical processes across trophic levels. This project report presents the implementation of a multi-trophic controlled-environment agriculture demonstrator that integrates computer-vision-based monitoring with established sensor infrastructure for aquaculture, poultry, plants, microalgae, duckweed, and insect modules. Stereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth, while continuous water-quality and environmental measurements (e.g., pH, dissolved oxygen, nitrate, ammonium, temperature, CO$_2$) provide complementary process data. These data streams are synchronized within a shared database architecture to enable cross-module evaluation of nutrient dynamics, growth progression, and operational stability under real facility conditions. The implemented framework demonstrates how computer vision can extend conventional sensor-based monitoring by directly capturing biological performance indicators across aquatic, terrestrial, and microbial domains. While advanced predictive modeling and full digital twin simulation remain future development steps, the realized data-integration architecture establishes a structural foundation for the systematic evaluation of circular indoor food-production systems. The demonstrator illustrates how multimodal monitoring can support nutrient recirculation, transparency of biological variability, and data-driven assessment within controlled multi-trophic environments.

Plant phenotyping relevance

植物成長をステレオ画像およびRGB-Dで非侵襲的に定量するコンピュータビジョン監視基盤を実装しており、植物フェノタイピングが統合監視システムの主要な技術要素である。

Code and data availability

The supplied blocks describe the demonstrator's plant phenotyping (RGB-D imaging, SVM segmentation, leaf area/canopy height metrics) and fish/poultry CV pipelines, but contain no data availability statement, no deposited datasets or images, and no author code repository or public URL. No paper-specific public asset is.

No evidence-backed public reproduction asset is currently recorded.