A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms

• Published in: Computers and electronics in agriculture Vol. 172; p. 105334
• Main Authors: Costa, Lucas, Nunes, Leon, Ampatzidis, Yiannis
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2020; Elsevier BV
• Subjects: algorithms; Big data; Cameras; Citrus; Color imagery; Crops; data collection; Estimation; Genetic algorithm; Genetic algorithms; grapes; Hyperspectral; Low cost; Multispectral; NDVI; normalized difference vegetation index; Normalized difference vegetative index; phenotype; Remote sensing; RGB; Sugarcane; unmanned aerial vehicles; Vegetation; Vegetation index; Vegetation index; Genetic algorithm; Hyperspectral; Big data; RGB; Multispectral; NDVI
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2020.105334

•A new visible NDVI (vNDVI) was developed using genetic algorithms.•It was evaluated in three different crops (citrus, grapes, and sugarcane).•The vNDVI accurately estimates NDVI values from RGB data.•It can provide a low-cost alternative for plant phenotyping. Several vegetation indices have been developed, with the normalized difference vegetation index (NDVI) been the most studied and commonly used. To generate an NDVI map, a relatively high-cost multispectral sensor is required; but currently, most UAVs are equipped with low-cost RGB cameras. For that reason, other indices that utilize RGB data have been developed to generate maps similar to NDVI and minimize the data acquisition cost, such as the triangular greenness index (TGI) and the visible atmospheric resistant index (VARI). However, several studies found that these indices cannot be recommended as reliable general-purpose crop health indicators. This study utilizes a genetic algorithm to develop a new visible index (visible NDVI; vNDVI) that estimates NDVI values of vegetation from uncalibrated RGB cameras mounted on UAVs (or other remote sensing platforms). Three experiments were conducted to create and validate the proposed index. First, the NDVI values generated from a multispectral camera were compared with the NDVI values generated by a hyperspectral camera. In the second experiment, the vNDVI formula was created using a genetic algorithm. The third experiment validates the proposed vNDVI, generated from two uncalibrated RGB cameras, in three different crops (citrus, grapes, and sugarcane). The proposed vNDVI proved to be highly accurate on estimating NDVI values by just using RGB cameras, with an overall mean percentage error of 6.89% and a mean average error of 0.052 in all three crops, providing a low-cost alternative for remote sensing and plant phenotyping.