Development and testing of an IoT phenotyping system for crops

Abstract

An autonomous IoT crop phenotyping system has been developed that integrates microclimatic, optical, and soil measurements with low-energy LoRaWAN connectivity. The ESP32-S3 node, lifepower, and periodic surveys (1 hour) ensure long-term operation. The optical module is based on the AS7262/AS7263 Fresnel lens spectrometers; the PHAR metrics are validated relative to the LI-190SB quantum sensor. According to field measurements in 2024. The diurnal profiles match, and the spectral features - PPFD regression model explains 89% of the variance (R2=0.89). The three-block architecture (aboveground/underground/control) is complemented by a modular infrared CO2 gas analyzer and a ToF laser sensor for calculating plant biomass growth and potential prediction of phenophases. It is shown that an inexpensive sensor assembly provides a reproducible assessment of biophysical parameters sufficient for rapid diagnosis of crop heterogeneity and subsequent integration with productivity models. Keywords: PHENOTYPING, INTERNET OF THINGS, IoT, AGROECOLOGICAL MONITORING, PRECISION AGRICULTURE, CROP HETEROGENEITY, REMOTE SENSING, LORAWAN, PAR

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