Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging
2015
Vesali, Farshad | Omid, Mahmoud | Kaleita, Amy | Mobli, Hossein
In this study a new android app for smartphones to estimate chlorophyll content of a corn leaf is presented. Contact imaging was used for image acquisition from the corn leaves which captures the light passing through the leaf directly by a smartphone's camera. This approach would eliminate the needs for background segmentation and other pre-processing tasks. To estimate SPAD (Soil Plant Analysis Development) values, various features were extracted from each image. Then, superior features were extracted by stepwise regression and sensitivity analysis. The selected features were finally used use as inputs to the linear (regression) and neural network models. Performance of the models was evaluated using the images taken from a corn field located in West of Ames, IA, USA, with Minolta SPAD 502 Chlorophyll Meter. The R2 and RMSE values for the linear model were 0.74 and 6.2. The corresponding values for the neural network model were 0.82 and 5.10, respectively. Finally, these models were successfully implemented on an app named SmartSPAD on the smartphone. After installing the developed app on the smartphone, the performance of the models were evaluated again using a new independent set of data collected by SmartSPAD directly from maize plants inside a greenhouse. The SmartSPAD estimation compared well with the corresponding SPAD meter values (R2=0.88 and 0.72, and RMSE=4.03 and 5.96 for neural network and linear model, respectively). The developed app can be considered as a low cost alternative for estimating the chlorophyll content especially when there is a demand for high availability.
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