Deep Count: Fruit Counting Based on Deep Simulated Learning

By Maryam Rahnemoonfar, Clay Sheppard. Italian National Conference on Sensors, 2017

491 citations recorded by the source corpus.

Recent years have witnessed significant advancement in computer vision research based on deep learning. Success of these tasks largely depends on the availability of a large amount of training samples. Labeling the training samples is an expensive process. In this paper, we present a simulated deep convolutional neural network for yield estimation. Knowing the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits or flowers by workers is a very time consuming and expensive process and it is not practical for big fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. Our network is trained entirely on synthetic data and tested on real data. To capture features on multiple scales, we used a modified version of the Inception-ResNet architecture. Our algorithm counts efficiently even if fruits are under shadow, occluded by foliage, branches, or if there is some degree of overlap amongst fruits. Experimental results show a 91% average test accuracy on real images and 93% on synthetic images.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/51579a8a40aea8dc6b9cdea9499dc27c2797499d

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Citations in the corpus

Fruit recognition from images using deep learning (2017, background): “Similarly, in paper [20] we can see a network trained on synthetic images that can count the number of fruits in images without actually detecting where they are in the image.”
A Review of Convolutional Neural Network Applied to Fruit Image Processing (2020, background): “Rahnemoonfar and Sheppard [94] presented a simulated deep CNN for automatic yield estimation based on robotic agriculture to help farmers in decisions for cultivation practices, plant disease prevention, and the size of the harvest labor force.”
Plant Disease Detection and Classification by Deep Learning (2019, methodology): “Leaf and fruit counting were also performed by deep CNN in [44,45] and [46] respectively.”
YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3 (2020, methodology): “Rahnemoonfar et al. [21] used a modiﬁed Inception-ResNet architecture [22] for fruit counting.”
Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection (2019, background): “Convolutional neural networks (CNNs) have performed well in crop classiﬁcation tasks [5], fruit counting, yield prediction [6], disease detection [7–9], and vision tasks in general [9,10].”
Unmanned Aircraft System (UAS) Technology and Applications in Agriculture (2019, background): “It has been shown by Rahnemoonfar and her colleagues that computer vision algorithms are able to count ripened tomatoes with a 91% accuracy from manually taken images of tomato plants [57].”
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review (2020, background): “In fruit recognition, researchers applied deep learning convolutional neural networks (CNN) to the visual techniques of agricultural robots (Yang et al., 2016; Bargoti and Underwood, 2017; Chen et al., 2017; Rahnemoonfar and Sheppard, 2017; Tahir and Badshah, 2018).”
IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, background): “For, e.g., for the purposes of activity recognition, the authors in [41] found that a CNN trained for activity recognition on one dataset failed to perform well on others or in [42] where the deep learning network performs poorly when the color of tomatoes is different from what it was trained on.”