Department of Artificial Intelligence

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A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field
(Association for Computing Machinery, 2020) Owomugisha, Godliver; Mugagga, K. B. Pius; Melchert, Friedrich; Mwebaze, Ernest; Quinn, A. John; Biehl, Michael
We present our initial proof of concept study towards the development of a low-cost 3-D printed smartphone add-on spectrometer. The study aimed at developing a cheap technology (less than 5USD) to be used for detection of crop diseases in the field using spectrometry. Previously, we experimented with the problem of disease diagnosis using an off-the-shelf and expensive spectrometer (approximately 1000 USD). However, in real world practice, this off-the-shelf device cannot be used by typical users (smallholder farmers). Therefore, the study presents a tool that is cheap and user friendly. We present preliminary results and identify requirements for a future version aiming at an accurate diagnostic technology to be used in the field before disease symptoms are visibly seen by the naked eye. Evaluation shows performance of the tool is better than random however below performance of an industry grade spectrometer. CCS CONCEPTS • Applied computing → Physical sciences and engineering; • Computing methodologies → Machine learning. KEYWORDS Low-cost, Spectrometery, Crop disease, Diagnosis, 3-D printed, Smartphone