An FPGA‐based smart camera for accurate chlorophyll estimations

다국어 초록 (Multilingual Abstract)

In this work, a new chlorophyll estimation approach based on the reflectance/transmittance from the leaf being analyzed is proposed. First, top/underside images from the leaf under analysis are captured, then, the base parameters (reflectance/transmittance) are extracted. Finally, a double‐variable linear regression model estimates the chlorophyll content. To estimate the base parameters, a novel optical arrangement is presented. On the other hand, in order to provide a portable device suitable for chlorophyll estimation under large scale food crops, we have implemented our optical arrangement and our algorithmic formulation inside an field‐programmable gate array (FPGA)‐based smart camera fabric. Experimental results demonstrated that the proposed approach outperforms (in terms of accuracy and processing speed) most previous vision‐based approaches, reaching more than 97% accuracy and delivering fast chlorophyll estimations (near 5 ms per estimation).
In this work, a novel chlorophyll estimation approach based on reflectance and transmittance is proposed. In order to estimate the base parameters (reflectance/transmittance) a novel optical arrangement is presented and our optical arrangement and the algorithmic part were implemented inside a smart camera fabric. It is demonstrated that the proposed algorithm outperforms (in terms of accuracy and processing speed) most previous vision‐based approaches, reaching over 97% accuracy under food crops and achieving fast chlorophyll estimations (near than 5ms per estimation).