Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification

Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c–Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and 59.50% (optimally determined principal component

Using hyperspectral remote sensing to map grape quality in 'Tempranillo' vineyards affected by iron deficiency chlorosis

The objectives of this work wereto investigate the relationships between chlorophyll a+b concentration in leaves (Cab) and grape composition parameters in vineyards affected by iron chlorosis, andstudy whether the assessment of Cab from hyperspectral remote sensing imagery could be useful to map different potential quality zones in these vineyards.A field trial was conducted in a vineyard with the chlorosis susceptible cultivar. 'Tempranillo', over '110 Richter', located in Northern Spain. Three experimental treatments were applied: 0, 2, and 4 foliar sprayings with a ligninsulphonate derived product (10 % water soluble Fe) in a randomized design with 3 replications. The yield and grape composition parameters at harvest were measured for each base-plot (10x10 m in size), and related with chlorophyll concentration in leaves. On the other hand, a total of 24 'Tempranillo' commercial vineyards were identified for field and airborne data collection with CASI hyperspectral sensor, comprising 103 study areas of 10x10 m in size. A total of 1467 leaves were collected for determining pigment concentration and optical properties. Several narrow-band vegetation indices were calculated from leaf reflectance spectra. Results of trial showed that the lack of pigmentation in leaves was a major factor limiting grape ripening. Significant linear regressions between Cab and total soluble solids concentration and colour density of the must were detected. Estimation of Cab using the image-calculated TCARI/OSAVI through the PROSPECT-rowMCRM model simulation for all study zones, including the specific ligninsulphonate experiment, demonstrated the potential of hyperspectral imagery for mapping Cab in vineyards for chlorosis detection using remote sensing methods. Given the described relationship between Cab and quality parameters in vineyards affected by iron chlorosis, high-spatial resolution imagery with narrow bands might enable the segmentation in areas of potential quality in the framework of precision viticulture.

Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method

Downy mildew is a major disease of grapevine. Conventional methods for assessing crop diseases are time-consuming and require trained personnel. This work aimed to develop and validate a new method to automatically estimate the severity of downy mildew in grapevine leaves using fuzzy logic and computer vision techniques. Leaf discs of two grapevine varieties were inoculated with Plasmopara viticola and subsequently, RGB images were acquired under indoor conditions. Computer vision techniques were applied for leaf disc location in Petri dishes, image pre-processing and segmentation of pre-processed disc images to separate the pixels representing downy mildew sporulation from the rest of the leaf. Fuzzy logic was applied to improve the segmentation of disc images, rating pixels with a degree of infection according to the intensity of sporulation. To validate the new method, the downy mildew severity was visually evaluated by eleven experts and averaged score was used as the reference value. A coefficient of determination (R2) of 0.87 and a root mean squared error (RMSE) of 7.61 % was observed between the downy mildew severity obtained by the new method and the visual assessment values. Classification of the severity of the infection into three levels was also attempted, achieving an accuracy of 86 % and an F1 score of 0.78. These results indicate that computer vision and fuzzy logic can be used to automatically estimate the severity of downy mildew in grapevine leaves. A new method has been developed and validated to assess the severity of downy mildew in grapevine. The new method can be adapted to assess the severity of other diseases and crops in agriculture.European Commission 828940Spanish Government PID2020-119478GB-I00Universidad de La Rioja 1150/2020 Gobierno de La Rioj

Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex

Foliar symptoms of grapevine leaf stripe disease (GLSD, a disease within the esca complex) are linked to drastic alteration of photosynthetic function and activation of defense responses in affected grapevines several days before the appearance of the first visible symptoms on leaves. The present study suggests a methodology to investigate the relationships between high-resolution multispectral images (0.05 m/pixel) acquired using an Unmanned Aerial Vehicle (UAV), and GLSD foliar symptoms monitored by ground surveys. This approach showed high correlation between Normalized Differential Vegetation Index (NDVI) acquired by the UAV and GLSD symptoms, and discrimination between symptomatic from asymptomatic plants. High-resolution multispectral images were acquired during June and July of 2012 and 2013, in an experimental vineyard heavily affected by GLSD, located in Tuscany (Italy), where vines had been surveyed and mapped since 2003. Each vine was located with a global positioning system, and classified for appearance of foliar symptoms and disease severity at weekly intervals from the beginning of each season. Remote sensing and ground observation data were analyzed to promptly identify the early stages of disease, even before visual detection. This work suggests an innovative methodology for quantitative and qualitative analysis of spatial distribution of symptomatic plants. The system may also be used for exploring the physiological bases of GLSD, and predicting the onset of this disease.

Development of canopy vigour maps using UAV for site-specific management during vineyard spraying process

Site-specific management of crops represents an important improvement in terms of efficiency and efficacy of the different labours, and its implementation has experienced a large development in the last decades, especially for field crops. The particular case of the spray application process for what are called “specialty crops” (vineyard, orchard fruits, citrus, olive trees, etc.)FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205)This research was partially funded by the “Ajuts a les activitats de demostració (operació 01.02.01 de Transferència Tecnològica del Programa de desenvolupament rural de Catalunya 2014-2020)” and an FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under the LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205).This research was partially funded by the “Ajuts a les activitats de demostració (operació 01.02.01 de Transferència Tecnològica del Programa de desenvolupament rural de Catalunya 2014-2020)” and an FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205)Postprint (updated version

Combining Hyperspectral Imaging and Small Unmanned Aerial Systems for Grapevine Moisture Stress Assessment

It has been shown that mild water deficit in grapevine contributes to wine quality, in terms of especially grape and subsequent wine flavor. Water deficit irrigation and selective harvesting are implemented to optimize quality, but these approaches require rigorous measurement of vine water status. While traditional in-field physiological measurements have made operational implementation onerous, modern small unmanned aerial systems (sUAS) have presented the unique opportunity for rigorous management across vast areas. This study sought to fuse hyperspectral remote sensing, sUAS, and sound multivariate analysis techniques for the purposes of assessing grapevine water status. High-spatial and -spectral resolution hyperspectral data were collected in the visible/near-infrared (VNIR; 400-1000nm) and short-wave infrared (SWIR; 950-2500 nm) spectral regions across three flight days at a commercial vineyard in the Finger Lakes region of upstate New York. A pressure chamber was used to collect traditional field measurements of stem water potential (ψstem) during image acquisition. We completed some preliminary exploration of spectral smoothing, signal-to-noise ratio, and calibration techniques in forging our experimental design. We then correlated our hyperspectral data with a limited stress range (wet growing season) of traditional measurements for ψstem using multiple linear regression (R2 between 0.34 and 0.55) and partial least squares regression (R2 between 0.36 and 0.39). We demonstrated statistically significant trends in our experiment, further qualifying the potential of hyperspectral data, collected via sUAS, for the purposes of grapevine water management. There was indication that the chlorophyll and carotenoid absorption regions in the VNIR, as well as several SWIR water band regions warrant further exploration. This work was limited since we did not have access to experimentally-controlled plots, and future work should ensure a full range of water stress. Ultimately, models will need validation in different vineyards with a full range of plant stress

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.Peer ReviewedPostprint (published version