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AgTechIoT & Industrial
FarmSense
AI Multi-Spectral Crop Sensor based on LoRaWAN
Self-powered field sensor that monitors crop health indices and predicts irrigation needs via edge AI.
NDVI Accuracy
±0.02
Solar Powered
Yes
FarmSense
Client
An agtech startup creating a solar-powered sensor for row crops that provides real-time vegetation index data without requiring battery changes during growing season.
Challenge
Developing a self-powered, weatherproof field sensor that can measure multispectral reflectance (NDVI, NDRE, PRI) reliably under varying sunlight conditions and transmit data from remote fields with no cellular coverage.
Product Concept & Architecture
The STM32WLE5 LoRa SoC is paired with an AS7341 10-channel spectral sensor covering visible-to-NIR bands (415-940nm). A 2W polycrystalline solar panel charges a 2000mAh LiFePO4 battery.
Engineering Solution
The device performs spectral measurements at solar noon (optimal sun angle) using the STM32WLE5's ADC in oversampling mode for SNR >60dB. NDVI, NDRE, and PRI indices are computed on-device. The TinyML model correlates spectral indices with soil moisture and predicts irrigation needs.
Result
NDVI accuracy within ±0.02 of reference spectrometer. Zero battery changes needed in 8-month field trial.
Deep Tech FAQ
What makes the AI Multi-Spectral Crop Sensor based on LoRaWAN unique?+
The AI Multi-Spectral Crop Sensor based on LoRaWAN leverages custom hardware and edge AI to deliver industry-leading performance and efficiency.
What are the core technical benefits?+
Key benefits include low power consumption, real-time processing, robust connectivity, and a compact form factor tailored for deployment.
Can this architecture be adapted for other use cases?+
Absolutely. The underlying methodology is modular and can be retargeted for various applications with minimal redesign.
Project Profile
Services Delivered
Hardware DesignEmbedded Software
Core Technologies
LTE-M/NB-IoTTinyMLLoRaWAN
Silicon & Vendors
STMicroelectronicsAMS
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