Sustainable intensification of top fruit production through precision orchard management using novel remote sensing technologies

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Our recent work has demonstrated that photogrammetry and laser scanning techniques can efficiently quantify the size and structural condition of orchard trees which are correlated with yield. We have also developed the use of hyperspectral and thermal remote sensing to diagnose plant nutrient deficiencies and water deficit. This project will build upon this work and apply the sensing techniques to the key commercial scion and rootstock varieties of apple and pear growing under a range of environmental conditions. We will acquire ground-based (fixed and tractor-mounted) and airborne (from drones) digital photography, laser scanner, hyperspectral and thermal data from commercial orchards, together with field-based measures of tree physiology, structure and yield. These data will be used to develop efficient analytical algorithms capable of identifying the exact requirement for management interventions such as structural pruning, irrigation and fertiliser input for each tree. Repeat acquisitions of remotely-sensed data will permit outputs related to strategic (pre-growing season) and tactical (within-growing season) management interventions. This approach offers an objective and quantifiable way of acquiring information on large numbers of trees across an orchard and translating this into precise treatments for individual trees. We will work with growers and other stakeholders to ensure that the outputs of the project contribute significantly to maximising yield while minimising environmental impacts and production costs.