Browsing by Author "Poona, Nitesh"

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    Investigating 2-D and 3-D proximal remote sensing techniques for vineyard yield estimation
    (MDPI, 2019-08-22) Hacking, Chris; Poona, Nitesh; Manzan, Nicola; Poblete-Echeverria, Carlos
    Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one’s (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r2 = 0.950), which outperformed RGB imagery (r2 = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.
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    A synthesizing land-cover classification method based on Google Earth Engine : a case study in Nzhelele and Levhuvu catchments, South Africa
    (Springer, 2020-07-07) Zeng, Hongwei; Wu, Bingfang; Wang, Shuai; Musakwa, Walter; Tian, Fuyou; Mashimbye, Zama Eric; Poona, Nitesh; Syndey, Mavengahama
    This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Google Earth Engine (GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) have little effect on final land-cover classification result.
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    Vineyard yield estimation using 2-D proximal sensing : a multitemporal approach
    (International Viticulture and Enology Society, 2020-10-23) Hacking, Chris; Poona, Nitesh; Poblete-Echeverria, Carlos
    Vineyard yield estimation is a fundamental aspect in precision viticulture that enables a better understanding of the inherent variability within a vineyard. Yield estimation conducted early in the growing season provides insightful information to ensure the best fruit quality for the maximum desired yield. Proximal sensing techniques provide non-destructive in situ data acquisition for yield estimation during the growing season. This study aimed to determine the ideal phenological stage for yield estimation using 2-dimensional (2-D) proximal sensing and computer vision techniques in a vertical shoot positioned (VSP) vineyard. To achieve this aim, multitemporal digital imagery was acquired weekly over a 12-week period, with a final acquisition two days prior to harvest. Preceding the multitemporal analysis for yield estimation, an unsupervised k-means clustering (KMC) algorithm was evaluated for image segmentation on the final dataset captured before harvest, yielding bunch-level segmentation accuracies as high as 0.942, with a corresponding F1-score of 0.948. The segmentation yielded a pixel area (cm2), which served as input to across-validation model for calculating bunch mass (g). The ‘calculated mass’ was linearly regressed against the ‘actual mass’, indicating the capability for estimating vineyard yield. Results of the multitemporal analysis showed that the final stage of berry ripening was the ideal phenological stage for yield estimation, achieving a global r2 of 0.790.