Counting Apples and Oranges With Deep Learning: A Data-Driven Approach

By Steven W. Chen, Shreyas S. Shivakumar, Sandeep Dcunha, J. Das, Edidiong Okon, Chao Qu, Camillo Jose Taylor, Vijay R. Kumar. IEEE Robotics and Automation Letters, 2017

366 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/d3eb6e57f618cf0830678594efd7e67eec76a898

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Citations in the corpus

A Review of Convolutional Neural Network Applied to Fruit Image Processing (2020, methodology): “Chen et al. [27] adopted deep learning to map from input images to total fruit counting.”
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles (2017, background): “Several applications have used deep learning techniques for this purpose [12, 49, 50, 55, 56], providing robust systems for monitoring the state of the crops in order to maximize their productivity.”
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “Besides, CNNs are successfully used in the field of agriculture for recognition of plant species [63], yield management [94,95], weed detection [96], soil and water management [97], fruit counting [98], diseases and pest detection [82,99], evaluating the nutrient status of plants [100], and monitori”
A Survey on Food Computing (2018, methodology): “[Chen et al. 2017b] introduced a stacked attention network to learn joint space from images and recipes for cross-modal retrieval.”
A review of the use of convolutional neural networks in agriculture (2018, methodology): “, 2016), linear regression (Chen et al., 2017), large margin classifiers (LMC, Xinshao and Cheng, 2015) and macroscopic cellular automata (Song et al.”
Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease (2019, methodology): “in [8] have proposed a novel method using deep learning for counting the apples and oranges from the real-time images.”
Grape detection, segmentation and tracking using deep neural networks and three-dimensional association (2019, methodology): “The first attempts employed CNNs to perform pixel classification, followed by additional steps to segment individual fruits [5, 7].”
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).”
Deep learning in agriculture: A survey (2018, methodology): “, 2016), SVM Regression (Kuwata and Shibasaki, 2015), area-based techniques (Rahnemoonfar and Sheppard, 2017), texture-based regression models (Chen et al., 2017), LMC classifiers (Xinshao and Cheng, 2015), Gaussian Mixture Models (Santoni et al.”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, methodology): “For example, crop counting and yield estimation is an important topic of precision agriculture [141], [202].”