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Remote sensing with lightweight optical sensors is becoming a powerful tool to solve many problems in agriculture. Achieving the level of spatial and spectral resolutions required for this type of detection at an acceptable cost-benefit ratio has motivated the development of new sensors which must be lightweight to be carried by mobile robots either aerial or terrestrial. One new type of multiple head cameras has been developed by Agrowing, an Israeli company developing technology for digital agriculture. The aim of this paper is to analyse the geometric features of an Agrowing dual head camera trough calibration experiments. The sensor was calibrated following two options, depending on the cropping technique used to produce the 4 spectral bands. Different calibration techniques were also used and very accurate results were achieved. Experiments with data collected with a UAV also confirmed the results achieved with close range calibration.

Shadows are common in any kind of remote sensing images. Unmanned Aerial Vehicle – UAV with a light camera attached can acquire images illuminated either by direct sunlight or by diffuse light under clouds. Indeed, areas with pixels shaded by clouds must be detected and labelled in order to use this additional information for image analysis. Classification of health and diseased plants in permanent culture as the orange plantation field can present some errors due to tree cast shadow. So, hyperspectral or multispectral image classification can be improved by previous shadow detection. Some FPI hyperspectral camera, designed for agricultural applications is limited in the spectral range between 500 to 900 nm. Wavelengths in the region of blue light and in the SWIR spectral region have physical properties that enable the enhancement of shaded regions in the images. In this work some combinations of different spectral bands were evaluated in order to specify those suitable to detect shadows in agricultural field images. In this sense, considering that vegetation and soil are the two main kind of coverage in an agricultural field, we hypothesized that wavelengths near blue light and the longest near infrared available in the camera range are good choices. In both spectral regions soil and vegetation targets have small spectral differences which contribute to enhance the differences between shaded and illuminated regions in the image. Hyperspectral images acquired with a FPI hyperspectral camera onboard a UAV over a plantation of oranges were used to evaluate these spectral bands. The results showed that the wavelengths of aproximatelly 510 nm and 840 nm available in the FPI camera are the best to detect any type of shadows in the agricultural fields.

High spatial resolution remote sensing images acquired by drones are highly relevant data source in many applications. However, strong variations of radiometric values are difficult to correct in hyperspectral images. Honkavaara et al. (2013) presented a radiometric block adjustment method in which hyperspectral images taken from remotely piloted aerial systems – RPAS were processed both geometrically and radiometrically to produce a georeferenced mosaic in which the standard Reflectance Factor for the nadir is represented. The plants crowns in permanent cultivation show complex variations since the density of shadows and the irradiance of the surface vary due to the geometry of illumination and the geometry of the arrangement of branches and leaves. An evaluation of the radiometric quality of the mosaic of an orange plantation produced using images captured by a hyperspectral imager based on a tunable Fabry-Pérot interferometer and applying the radiometric block adjustment method, was performed. A high-resolution UAV based hyperspectral survey was carried out in an orange-producing farm located in Santa Cruz do Rio Pardo, state of São Paulo, Brazil. A set of 25 narrow spectral bands with 2.5 cm of GSD images were acquired. Trend analysis was applied to the values of a sample of transects extracted from plants appearing in the mosaic. The results of these trend analysis on the pixels distributed along transects on orange tree crown showed the reflectance factor presented a slightly trend, but the coefficients of the polynomials are very small, so the quality of mosaic is good enough for many applications.

Brazil is one of the world leaders in citrus plantation, and the production of orange juice is economically important in the export scenario, being regarded as a fundamental agricultural commodity in Brazil. The worst citrus disease is Greening, or Huanglongbing (HLB), a bacterial disease which cannot be cured and to which no plant variety is immune or resistant. Currently, control of HLB is through the inspection of orchards and the immediate elimination of plants displaying HLB symptoms, plus chemical or biological control of the insect vector (psyllid). The HLB disease has high economic impact on Brazilian and world citriculture, due to the extreme damage to crops. Based on remote sensing techniques, the mapping of diseased and healthy citrus plants from hyperspectral images was carried out, generating a product that could help in the monitoring and control of HLB in Brazilian citrus orchards. The methodology to produce a health map goes through the following stages: aerial image acquisition, radiometric field measurements, hyperspectral cube orientation, and data analysis for detection of HLB in citrus. The field survey was performed in Guacho Farm- Brazil and hyperspectral images were acquired by a Rikola camera onboard a light aircraft, obtaining images with 0.50 m of Ground Sample Distance (GSD). The hyperspectral cubes were classified with Spectral Angle Mapper (SAM) algorithm to produce the health map. Plants infected with HLB were detected with an accuracy 61.2%, the validation of the health map was verified by samples were analysed in the laboratory to confirm HLB.