Estimating Fruit Crop Yield through Deep Learning

Deep learning can bring significant improvements to a range of research areas and application domains. Computer vision is one of the key areas that can benefit from deep learning. In particular, object detection and classification can benefit from such approaches. In this work we focus on counting the number of images of individual fruit that can be used by fruit growers to estimate fruit crop yields. To achieve this, we apply two different state of the art convolutional neural networks (CNNs): Faster R-CNN and Single Shot Detection (SSD). CNNs depend on data for training and tuning of the models. In this paper we establish a dataset containing images for a range of fruit types. Using this data, we apply the models to identify the number of fruit in images and the challenges that are encountered. We present the experimental results of applying these approaches and illustrate their performance including the accuracy, time and loss when counting fruit on trees. We consider the future challenges in scaling this work to deal with more complex issues around fruit estimation at scale.