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This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing (HLB) disease in citrus trees. Visible-near infrared (440–900 nm) and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data analysis revealed that the average reflectance values of the healthy trees in the visible region were lower than those in the near infrared region, while the opposite was the case for HLB-infected trees. Moreover, 560 nm, 710 nm, and thermal band showed maximum class separability between healthy and HLB-infected groups among the evaluated visible-infrared bands. Similarly, analysis of several vegetation indices indicated that the normalized difference vegetation index (NDVI), Vogelmann red-edge index (VOG) and modified red-edge simple ratio (mSR) demonstrated good class separability between the two groups. Classification studies using average spectral reflectance values from the visible, near infrared, and thermal bands (13 spectral features) as input features indicated that an average overall classification accuracy of about 87%, with 89% specificity and 85% sensitivity could be achieved with classification models such as support vector machine for trees with symptomatic leaves.

This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water needs, weeds, and diseases. The general goal was proposing UAV-based technological solutions for a better use of agricultural and forestry resources and more efficient production with relevant economic and environmental benefits.

A yield monitoring system was used to collect yield data from a commercial citrus canopy shaker during the 2008 harvesting season. A computer algorithm was developed to preprocess the yield data before estimating field efficiency (E f ) and field machine index (FMI) measures of the mechanical harvesting equipment. Total time of the harvesting operation was calculated and divided into primary and support functions, which corresponded to the effective harvesting time and machine time losses, respectively. Time losses related to row-end turning were determined using an algorithm based on linear regression and geometrical methods. Each component of the harvesting operation was then expressed as a percentage of total field time. It was observed that FMI varied from 80% to 98% with 4% standard deviation. Turning time varied from 3% to 8% of the total operational time. Further data analysis showed an exponential relationship between FMI and row-end turning time with R 2 =0.97. It was also observed that the actual travel distance and the effective time of operation have linear relationships with the theoretical distance of operation with R 2 =0.96 and 0.93, respectively.

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