Modern agriculture is developing under conditions of climate variability, resource scarcity, and economic instability, which demand solutions that balance sustainability with efficiency. Recent advances emphasize technologies for monitoring plant physiological status, detecting stress, and optimizing water and resource use. Among the most significant developments are sensor networks for chlorophyll fluorescence induction (CFI, Kautsky effect) and environmental parameters, complemented by imaging techniques for disease detection and cultivar characterization. Ground-based measurements also play a critical role in calibrating satellite observations, thereby linking local field data with large-scale remote sensing. Early visual diagnostic practices, which historically preceded modern plant phenotyping, provided the conceptual basis for these approaches and have since evolved into digital instruments of precision agriculture. Today, the convergence of sensor technologies, Internet of Things (IoT) platforms, and machine learning enables the development of intelligent decision support systems (DSSs). These systems support adaptive, data-driven crop management and are establishing new standards for precision agriculture under the combined pressures of climate change and global market uncertainty.
Yevheniia Babenko (Fri,) studied this question.