Introduction Digital technologies in agriculture are often associated with sophisticated, high-end, or network-capable systems based on robotics, remote sensing, or IoT. These systems, however, remain financially and logistically inaccessible for many researchers and farmers, particularly in remote regions and the Global South. This paper presents a contrasting, yet complementary, bottom-up approach by advocating the adoption of low-cost DIY systems to enable site-specific crop management and support climate-adaptive decisions. Methods The Environmental Variables Explorer (EVE) is presented as a low-cost, open-source platform that integrates modular microcontroller-based sensing, reliable low-power operation, timekeeping, and non-volatile data storage. The platform supports two interoperable, end-to-end workflows under full user control: EVE-Offline, a stand-alone logger that stores measurements locally in FRAM and allows wireless data retrieval via a custom Android Bluetooth app; and EVE-Online, an ESP32-based node that transmits data via Wi-Fi to a self-hosted backend providing a web dashboard and CSV export on inexpensive shared hosting. To demonstrate the platform, EVE was implemented in both workflows as a compact weather station recording photosynthetically active radiation (PAR), air temperature, relative humidity (RH), and air pressure at user-defined intervals. Sensor performance was evaluated using co-location tests and independent reference stations. Results The custom low-cost PAR sensor showed strong reproducibility and close agreement with reference measurements. A co-location validation against a TOMST TMS-4 reference confirmed near 1:1 temperature agreement under identical conditions. Field deployments further demonstrated stable temporal dynamics across variables and reliable end-to-end data handling in both offline and online workflows. Discussion Through its openly accessible hardware designs, firmware, backend code, and build instructions, EVE provides a practical alternative to locked-in commercial systems. It enables cost-effective, bottom-up monitoring for researchers, farmers, educators, and community initiatives, while its modular platform can be extended with additional sensors and locally validated analyses without changing the underlying workflows. These results show that improving agricultural productivity while minimizing environmental impacts requires not only cutting-edge, data-heavy technologies but also autonomous, customizable tools that support user-driven innovation from the bottom up, particularly in resource-limited contexts.
Kretzschmar et al. (Wed,) studied this question.