Estimating Pesticide Concentration with Smartphone

Estimating Pesticide Concentration with Smartphone

Estimating pesticide concentration in fruits-vegetables with smart phone camera

This is a multi-disciplinary research with an aim to empower everyone to be able to measure pesticide concentration present in the vegetables and fruits they buy by simply using their smartphones and a filter paper. The project is in collaboration with Dr. Basant Giri of Kathmandu Institute of Applied Sciences (KIAS).

Involvement from NAAMII: Bishesh Khanal and Bidur Khanal

Summary:

Paper-based analytical devices (PADs) are new class of devices/methods/tests that are gaining popularity in clinical diagnostics, environmental pollution monitoring and other applications. The PADs are low-cost, easy to use at point of need and are also praised for not requiring highly skilled personnel to use [1].

Most of the PADs use colorimetric detection method. In colorimetric detection method, the target analyte reacts with a reagent producing characteristic color for the given assay. Just visualizing the color by naked eye may give qualitative result. For quantitative result, normal or smartphone camera takes photo of the assay and the resulting image is processed with software such as ImageJ and Adobe Photoshop. Example PAD in Figure 1. Generally, the image is analyzed based on RGB or HSB/V color space. As such analysis can only give signal of an assay in broad color range, colorimetric paper assays are less sensitive when compared to their conventional spectrophotometer-based assays. In spectrophotometric measurements, signal of a color at given wavelength can be measured, while in paper-based method the resolution of wavelength measured in the image is limited by camera sensor and digitization. Therefore, the later method is less sensitive and less selective as it averages out signal from neighboring wavelengths of light.

Providing a more sensitive and selective measurement from the images obtained from normal phone camera is an inverse problem. One way to tackle this could be to develop together a computer vision algorithm and the paper analytic device to provide a particular pattern or colors. We are exploring computer vision and machine learning algorithm together with the device development. The computer vision and machine learning algorithm will be explored together with Bishesh Khanal at NAAMII, while the analytic device development is currently being done at KIAS led by Basant Giri. [1] Sharma, Niraj, Toni Barstis, and Basant Giri. “Advances in paper-analytical methods for pharmaceutical analysis.” European Journal of Pharmaceutical Sciences 111 (2018): 46-56. full-text-PDF

Project Category: Analytical Chemistry, Image Processing, Machine Learning