The determination of optimal growth conditions for crops is crucial to avoid losses in agricultural production due to preventable factors. Although multiple devices have been developed to determine the chemistry of soil, and correlate them with plant health, these sensors cannot provide individualised information in real-time. As such, new devices for the in vivo quantification of plant biomarkers are necessary to allow an early response by the farmers. In particular, the design of implantable sensors represents a promising technology to enable the in-situ evaluation of key environmental conditions such as soil pH, pollution, or dryness. However, current developments in the field require expensive equipment, either for the fabrication of sensors, or measurement processes, limiting their applications within real-world environments. This work presents for the first time a low-cost and accessible approach for the fabrication of miniaturised pH biosensors, that can be fabricated using open-source and low-cost equipment. This device was developed by electrodepositing Ruthenium oxide nanoparticles onto thin copper films using a microcontroller-based potentiostat (>£50). A cellulose-based coating was then incorporated by an aerosol-based method, developed using off-the-shelf devices. The combination of these two low-cost deposition methods allowed the fabrication of nanometrically thick pH biosensors. The results were proven to be similar to the ones achieved by standardised laboratory equipment. The final setup combined the pH sensing layer of ruthenium oxide and cellulose, with a microcontroller that could send the collected data wirelessly to online servers for IoT applications. A proof-of-concept device was implanted inside a tomato plant, including multiple environmental sensors, and the changes in pH inside the stem, a well-known health biomarker, could be measured continuously. These results represent a step forward towards the practical application of implantable sensors in crop production, offering a plethora of applications in smart farming and plant research within low-resource settings.