Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Abstract

Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.

Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery

“…This study is one of the first attempts to detect and quantify native palm species in a complex forest such as Amazonia. Most of the previous studies that allow detection and quantification using a segmentation step before conducting the classification have been performed either in plantations [28,57] or in open forests [32], areas where the studied features are easily discriminated due to the high contrast of them with the background. In all these studies, the presence of bare soil or very little presence of small plants in the background makes easier the detection and quantification process.…”

“…However, the vegetation in these ecosystems is sparse and less diverse than in tropical forests [17,21].Some emerging analytical techniques may be particularly useful for mapping tree biodiversity in moist tropical forests. Some options for object detection in high-resolution imagery are possible by combining machine learning and computer vision techniques such as object-based image analysis methodologies (OBIA) [26], bags of visual words [27], and deep learning techniques [28]. Among these approaches, OBIA has already been applied successfully in ecosystem types such as dry tropical forests and temperate forests using UAV imagery [26].…”

UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence

“…Nevalainen et al [36] analyzed the possibility to detect individual trees in boreal forests using UAV-based hyperspectral imaging and reported a 93% accuracy on 4151 reference trees. Csillik et al [26] reported an overall accuracy of 96% on detecting citrus trees using a simple convolutional neural network and a refinement algorithm based on super-pixels. Using UAV-based LiDAR (Light Detection and Ranging) data, Wallace et al [37] reported an accuracy of 98% on detecting Eucalyptus globulus trees.…”

Estimating and Examining the Sensitivity of Different Vegetation Indices to Fractions of Vegetation Cover at Different Scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

“…The UAS platform provides alternatives to space-borne platforms since optical data can be observed in a clear-high spatial/temporal resolution for the region of interest [23]. This technique has been used in research on ecology [24], precision agriculture/forestry [25,26] and even analyses for estimating vegetation cover [27][28][29]. UAS was successful [27] in clearly estimating vegetation fractions and flower fractions in crop fields with the changing VIs, and work by Chen et al [28] showed that utilizing UAS-captured imagery may clearly detect grassy vegetation covers due to its high-resolution data.…”