Abstract
The world is aggressively taking initiative towards precision and smart agriculture. The aim is to increase the crop yield by avoiding minimal loss through early detection of abiotic stresses and disease in orchards. Proximal sensors are more robust, precise and provide simple data because of utilization of optical sensing techniques to detect efficiently the site-specific variability of vine physiology. Optical sensing techniques are commonly applied to capture the Normalised Difference Vegetation Index (NDVI) for plant canopy assessment. The initiative of this research is to evaluate the performance of two proximal sensors (OptRx by Ag Leader Technology®, USA) and a novel model by Spraylogics GmbH (Innsbruck, AT)) in a greenhouse environment to detect the abiotic stresses on grapevine plants. Four stressors were applied to the plants systematically. Plants were selected randomly and placed in such a pattern that five plants were placed 20 cm apart and then nine plants with no spacing. The carrier platform was moved manually at average speeds of 0.3 m/s and 0.8 m/s. Calibrated the sensors and the data were collected and processed in MATLAB. The results indicated that early detection of non-invasive copper and drought stress is possible and Spraylogics sensor performed well as compared to OptRx sensor. In future, the performance of both sensors will be evaluated in grapevine and apple orchards.