Technology in agriculture has advanced significantly over the past few
decades. However, small and medium-sized agricultural enterprises often face
challenges in accessing and adopting these technological innovations. In India, the
majority of farmers are marginal or smallholders cultivating up to 2 hectares of land.
Because the productivity of these agricultural systems is typically lower than that of
large-scale operations, it is essential to manage resource requirements precisely to
maximize benefits. Irrigation remains one of the most critical needs in all types of
agriculture. Appropriate and cost-effective irrigation methods can enable farmers to
improve yields while conserving resources. This study presents an integrated approach
combining hyperspectral remote sensing with an IoT-based manual irrigation system
tailored for small-scale agriculture. Hyperspectral imagery is leveraged to assess spatial
variability in crop health, soil moisture distribution, and stress indicators across fields,
providing actionable insights to optimize water application. The designed system
consists of an epicyclic gear train arrangement powered by solar energy and interfaced
with IoT technology. An advanced circuit incorporating an ESP8266 microcontroller is
implemented to monitor key environmental parameters, such as soil moisture content,
relative humidity, and air dry bulb temperature. The integration of hyperspectral data with real-time sensor measurements enables more informed irrigation scheduling and
targeted water delivery. The system offers programmable control to maintain ideal soil
moisture content at 80% saturation, supporting sustainable water management and
improved productivity for small and marginal farmers. This combined strategy
demonstrates the potential of merging hyperspectral remote sensing and IoT solutions
to advance precision irrigation practices in smallholder agriculture.
Keywords: Epicyclic gear train, External source, Farmer, Hand pump, IoT, Irrigation, Technology.
