Development and evaluation of an android-based mobile application version of the leaf color chart or LCC [leaf color chart] App

This paper presents the development an android-based mobile application simulating a rice chlorophyll meter for determining real-time nitrogen (N) requirement and evaluated its performance as a recommending tool for topdressing. At present, chlorophyll meters are commercially available but are too expensive for ordinary farmers. Hence, PhilRice [Philippine Rice Research Inst., Nueva Ecija, Philippines], in the past, developed the leaf color chart (LCC), a cheap but effective alternative to chlorophyll meters capable of diagnosing and recommending N requirements in real-time. However, farmers adoption rate of the LCC despite years of techno-promotion, was not so significant. With the current access to digital platforms, the underlying principle/process of the LCC was developed into an android-based mobile application to revive the useful tool. Initially, a conversion process for digital leaf images into dark green color index (DGCI) was developed, coded into a computer program and then used to identify the DGCI to the original 6-panel LCC to establish its correlation. Similarly, the correlation between the 6-panel LCC and SPAD readings were also established to finally determine a connection between DGCI and SPAD values by merging the two correlations. A prototype android-based mobile application (LCC App) was therefore developed with the merged correlation embedded that made use of smartphone's built-in cameras to capture rice leaf images, process it into DGCI and generate N-topdressing rates. Comparison between the DGCI against actual SPAD values showed strong correlation in both DS2017 and WS2018 field assessments. However, N-content of rice leaves sampled in DS2017 were found better correlated with DGCI than actual SPAD values. While yield evaluation trails using NSIC Rc216 with 5 treatments and 4 replications showed a very comparable yield performance between LCC and LCC App during WS2018 with the agronomic efficiency of applied N (AEn) under LCC App better than other treatments except one. However in DS2019, grain yield under LCC App surpassed all treatments but its AE sub N was now only better than LCC.