Evaluating In-situ Maize Chlorophyll Content Using an External Optical Sensing System Coupled with Conventional Statistics and Deep Neural Networks

Abstract Plant conditions can be monitored by direct-leaf measurements. For this purpose, several researchers have developed applications based on smartphone cameras, but the variable quality of smartphone cameras, even those of the same brand, produces non-standardized results. The present study describes a new and reliable technique that measures the chlorophyll content of maize leaves for plant monitoring on any smartphone. To check the accuracy of the data generated by the developed handheld optical sensing system, the obtained data were compared against those of an established chlorophyll-monitoring meter (a SPAD-502 monitor). The required SPAD, RGB, and global positioning system data were collected from maize fields (∼2 hectares) at the research farm of the Indonesian Coffee and Cocoa Research Institute. The collected data were analyzed using conventional statistics/regression analysis and a deep neural network (DNN). The inverse-distance weighted outputs were interpolated to generate zoning maps. In a conventional statistics/regression analysis, the chlorophyll levels were significantly correlated with the SPAD values (R2 = 0.82–0.84, root mean squared error [RMSE] = 2.95–3.05). However, after applying the DNN with 12 extracted input features, four hidden layers, and 637 parameters, the chlorophyll content estimation was significantly improved (R2 and RMSE = 0.89 and 2.6, respectively), and the zoning map generated by the developed system was nearly aligned with the SPAD zoning map. The findings confirm that this technique is applicable to all types of smartphones regardless of their camera properties and provides the light-aided intensity necessary for direct-leaves measurement. The locations of the collected leaves were simultaneously monitored in real-time to generate the mapping results.