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Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.UAVs allow growers to constantly monitor crop health status, estimate plant water needs, and even detect diseases [15][16][17]. They represent a low-cost method for image acquisition in high-resolution settings and are increasingly studied for agricultural applications. Nebiker et al. [18] reported one of the first successful application of UAVs combined with a low-cost multispectral camera for remote sensing to assess health of grapevine crops. Hunt et al. [19] analyzed UAV multispectral imagery for crop monitoring and they found a good correlation between leaf area index and the green normalized difference vegetation index (green NDVI). In order to achieve high spatial resolution of image data, the overlaps of the UAV images, and, hence, the flight path planning, must be critically selected [20]. Matese et al. [21] mapped the vigor of vineyards using the NDVI index from a high-resolution multispectral camera. Furthermore, UAVs were utilized to create plant inventories.Malek et al. [22] achieved promising results in detecting palm trees used an extreme learning machine classifier in UAV images.In citrus, multispectral imagery from UAVs were recently utilized to identify tree stresses and diseases. Garcia-Ruiz et al. [23] and Sankaran et al. [24] analyzed multispectral features of Huanglongbing (HLB) infected citrus trees for classification. Romero-Trigueros et al. [25] analyzed correlations of citrus physiology stresses and gas exchange status using multispectral UAV images. Csillik et al. [26] proposed a methodology to detect citrus and other crops trees from UAV images using a simple deep learning convolutional neural network (CNN).Deep learning algorithms (artificial intelligence based) are increasingly used in rem...

A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle (UAV). In the laboratory, a hyperspectral (400–1000 nm) imaging system was utilized for the detection of citrus canker in several disease development stages (i.e., asymptomatic, early, and late symptoms) on Sugar Belle leaves and immature (green) fruit by using two classification methods: (i) radial basis function (RBF) and (ii) K nearest neighbor (KNN). The same imaging system mounted on an UAV was used to detect citrus canker on tree canopies in the orchard. The overall classification accuracy of the RBF was higher (94%, 96%, and 100%) than the KNN method (94%, 95%, and 96%) for detecting canker in leaves. Among the 31 studied vegetation indices, the water index (WI) and the Modified Chlorophyll Absorption in Reflectance Index (ARI and TCARI 1) more accurately detected canker in laboratory and in orchard conditions, respectively. Immature fruit was not a reliable tissue for early detection of canker. However, the proposed technique successfully distinguished the late stage canker-infected fruit with 92% classification accuracy. The UAV-based technique achieved 100% classification accuracy for identifying healthy and canker-infected trees.