Development and Application of Internet of Things and Visual Det

Field observations in viticulture show that digital monitoring tools and automated systems are increasingly used in conjunction with analytical decision-support modules. Building on this practical experience, the chapter explores how Internet of Things (IoT) technologies, visual systems, and decision support systems (DSS) can be integrated into a digital framework for managing crop productivity. The proposed approach focuses on translating plant morphological traits, traditionally described in taxonomy, into measurable descriptors suitable for algorithmic analysis. Using grapevine (Vitis vinifera L.) as a case study, it demonstrates how ampelometric indicators, geometric parameters of leaves, vein angles, and sinus shapes, can be combined with spectral and physiological characteristics within an analytical model, or function as an autonomous diagnostic and analytical module in decision-support systems. This framework enables early disease detection, continuous assessment of plant condition, and optimization of resource use. The analytical models and procedural schemes developed within the study illustrate how digital morphology and biological observation can operate jointly in integrated systems of analysis and decision support, enhancing diagnostic precision and supporting sustainable management of agrobiocenoses.