Abstract
Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R2 = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.
Highlights
In this research, in order to refine the process of Leaf area index (LAI) estimation in rice using an RGB camera mounted on a Unmanned aerial vehicle (UAV), we examined whether estimation models developed by deep learning (DL) with the RGB images as input data could be an alternative to existing LAI estimation methods using
We attempted to improve the accuracy of LAI estimation in rice using an RGB camera mounted on a UAV by developing an estimation model using DL with the images as input data, and compared the estimation accuracies of the resulting model and other hands-on approaches
We examined whether models developed by DL using RGB
Summary
Leaf area index (LAI), which represents one half of the total green leaf area (i.e., half of the total area of both sides of all green leaves) per unit horizontal ground surface area [1], is a key vegetation parameter for assessing the mass balance between plants and the atmosphere [2,3], and plays an important role in crop growth estimation and yield prediction [4,5,6]. Accurate LAI estimation is an important to evaluate crop productivity. Direct LAI measurements have been performed by destructive sampling, but this approach requires a great deal of labor and time. It is often difficult to obtain a representative value of the plot because only a part of the plot can be surveyed by direct sampling. In recent years, various indirect methods for LAI estimation have been developed to solve these problems
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