Soybean canopy estimation using different image capture methods

The determination of crop canopy characteristics (vegetation cover, leaf area, and leaf area index) is usually obtained through costly methods or methods that require training and time. Based on this, the aim of this work was to determine whether different methods and angles for capturing images using a smartphone camera and fisheye lenses can predict information about the soybean canopy in a practical, fast, and low-cost way. Different methods of capturing images were carried out throughout the soybean cycle using smartphones, attached fisheye lenses, manual normalized difference vegetation index (NDVI) reading equipment, and destructive plant evaluations. The captured images were analyzed in the Canopeo app to determine the green cover fraction. Pearson’s correlation and regression models were used to study the association between NDVI and leaf area index (LAI). The data were compared in the vegetative and reproductive stage segments and throughout the crop cycle to determine whether the image acquisition methods were capable of estimating the variations at each crop stage. With the exception of images captured during the soybean’s reproductive stage, all methods proved to be suitable for evaluations and comparisons using the Canopeo app. Capturing images 1 m above the canopy and video were the methods that best estimated the NDVI and LAI of soybeans.