Yield prediction in apple orchards based on image processing

By A. Aggelopoulou, Dionysis D. Bochtis, Spyros Fountas, K. Swain, T. Gemtos, G. Nanos. Precision Agriculture, 2011

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Detection and counting of flowers on apple trees for better chemical thinning decisions (2019, background): “For example, Aggelopoulou and his collaborators (Aggelopoulou et al. 2011) first converted the image from RGB to a single channel measuring the distance to a pre-defined flower color.”
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards (2016, background): “Yield estimation also allows the grower to plan ahead of time their harvest logistics, crop storage, and sales (Aggelopoulou et al., 2011; Nuske, Wilshusen, Achar, Yoder, & Singh, 2014; Payne & Walsh, 2014).”
Agricultural Robotics: The Future of Robotic Agriculture (2018, background): “lective (micro-) spraying and laser weeding. Irrigation is another area where robots could assist in targeting water in the right place at the right time. Pre-harvest assessment and yield forecasting [56] by robotic sensory systems will further assist in choosing the right time to harvest the crop •”