Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images

Abstract

Leaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evolution of the LAI and percentage of land cover (%COV) in coffee plants, using pre-established equations and plant measurements obtained from generated 3D point clouds, combined with the application of the SfM algorithm to digital images recorded by a camera coupled to a UAV (unmanned aerial vehicle). The experiment was conducted in a coffee plantation located in southeastern Brazil. A rotary wing UAV containing a conventional camera was used. The images were collected once per month for 12 months. Image processing was performed using PhotoScan software. Regression analysis and spatial analysis were performed using R and GeoDa software, respectively. The resulting %COV data had R 2 and RMSE values of 89% and 3.41, respectively, while those for LAI had R 2 and RMSE of 88% and 0.47, respectively. Significant %COV results were obtained in the months of January, February and March of 2018. There was significant autocorrelation for the LAI values from January to May 2018, with most blocks in the central and centerwest regions presenting LAI values > 3.0. It was possible to monitor the temporal and spatial behavior of the LAI and %COV, allowing for the conclusion that this methodology generated results that are consistent with the literature.

Drone-Aided Detection of Weeds: Transfer Learning for Embedded Image Processing

“…Typically, the UAVs are equipped with an RGB camera capturing the crops underneath in a visual spectrum, and the machine vision algorithms are processing the images from this camera in order to get the desired output. Typically such processing is done offline after the mission is complete [13] since the frames stream is stored on an internal storage of UAV [14] or is transferred to the intermediate server during the mission by a wireless protocol [15]. Since both cases are not practically feasible as they require additional efforts and do not allow for fast decision making, it is important to consider inference being performed directly on the edge device [16].…”

Analysis of the Potential of Remote Piloted Aircraft in the Detection of Damage by Frost in Coffee Plants

“…RPA has been successfully used to evaluate different conditions of coffee plants, such as nitrogen content [8,9], disease [10], biophysical parameters [11], planting errors [12] and fruit detection and maturation [13,14]. However, assessing frost damage, there is still a considerable gap to be filled.…”

Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops

“…By collecting overlapping image sequences using a high-resolution camera mounted on a UAS and then inputting those images to an SfM algorithm, users effectively can create a three-dimensional (3D) point cloud of a scanned field. Particularly in precision agriculture, the SfM point cloud approach can be used to derive structural parameters of crops, such as plant height [3,4], canopy volume [5,6], and leaf area coverage [7,8], all of which could significantly help farmers to enhance agricultural management decisions [9].…”