Apple fruit size estimation using a 3D machine vision system

Gongal, Aleana; Karkee, Manoj; Amatya, Suraj

doi:10.1016/j.inpa.2018.06.002

Cited by 77 publications

(60 citation statements)

References 16 publications

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“…The results have demonstrated that the proposed technique is feasible for dense 3D measurement applications. In [57] the authors developed a machine vision system consisting of RGB and ToF camera to estimate the size of apples in tree canopies. With this system, they were able to not only measure the size of apple fruits but even to calculate the local coordinates of each fruit.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Different Remote Sensing Methods for 3D Modeling of Small Rock Outcrops

Mikita

Balková

Bajer

et al. 2020

Sensors

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This paper reviews the use of modern 3D image-based and Light Detection and Ranging (LiDAR) methods of surface reconstruction techniques for high fidelity surveys of small rock outcrops to highlight their potential within structural geology and landscape protection. LiDAR and Structure from Motion (SfM) software provide useful opportunities for rock outcrops mapping and 3D model creation. The accuracy of these surface reconstructions is crucial for quantitative structural analysis. However, these technologies require either a costly data acquisition device (Terrestrial LiDAR) or specialized image processing software (SfM). Recent developments in augmented reality and smartphone technologies, such as increased processing capacity and higher resolution of cameras, may offer a simple and inexpensive alternative for 3D surface reconstruction. Therefore, the aim of the paper is to show the possibilities of using smartphone applications for model creation and to determine their accuracy for rock outcrop mapping.

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Section: Discussionmentioning

confidence: 99%

Comparison of Different Remote Sensing Methods for 3D Modeling of Small Rock Outcrops

Mikita

Balková

Bajer

et al. 2020

Sensors

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“…Several techniques were also presented by Fellegari & Navid (2011) and Thipakorn, Waranusast, & Riyamongkol (2017) in image processing method for measuring object size and volume, but with orange and eggs respectively. The study of Gongal, Karkee, & Amatya (2018) provided a solution to apple size estimation using fusion of 2D and 3D camera. Similar studies in apple Copyright © 2019 Universitas Brawijaya and orange was provided by Kalantari (2014) using image-processing technique.…”

Section: Article Historymentioning

confidence: 99%

Size Classification of Tomato Fruit Using Thresholding, Machine Learning, and Deep Learning Techniques

2019

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The size of tomato fruits is closely related to the market segment and price. Manual sorting in tomato is very dependent on human interpretation and thus, very prone to error. The study presents thresholding, machine learning and deep learning techniques in classifying the tomato as small, medium and large based from a single tomato fruit image implemented using Open CV libraries and Python programming. Tomato images with different sizes are gathered where features like area, perimeter and enclosed circle radius are extracted. The experiment shows that using thresholding, a classification accuracy of 85.83%, 65.83% and 80% was achieved for area, perimeter and enclosed circle radius, respectively. For machine learning, the training accuracy rates were recorded as 94.00%-95.00% for SVM, 97.50-92.50% for KNN and 90.33-92.50% for ANN. Comparison of models revealed that SVM is the most model without over fitting. The deep learning approach, regardless of the algorithm, produced low performances with 82.31%-78.21%-55.97% trainingvalidation-testing accuracy for VGG16, 48.17%-41.44%-37.64% for InceptionV3 and 56.05%-44.96%-22.78% for ResNet50 models. Comparative analysis showed that machine learning technique bested the performance of the thresholding and deep learning techniques in classifying the tomato fruit size in terms of accuracy performance.

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“…Their contribution was the use of two clustering methods: semi-supervised (to separate the apple pixels from others in the input images) and unsupervised (to automatically identify the apples). Fruit size was estimated by Gongal et al (2018) using the 3D coordinates of pixels from images taken by a 3D-camera as a tool for harvesting robots. A fine-tuned model for apple flower detection was deployed by Dias et al (2018).…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique

et al. 2020

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Farmers require accurate yield estimates, since they are key to predicting the volume of stock needed at supermarkets and to organizing harvesting operations. In many cases, the yield is visually estimated by the crop producer, but this approach is not accurate or time efficient. This study presents a rapid sensing and yield estimation scheme using offthe-shelf aerial imagery and deep learning. A Region-Convolutional Neural Network was trained to detect and count the number of apple fruit on individual trees located on the orthomosaic built from images taken by the unmanned aerial vehicle (UAV). The results obtained with the proposed approach were compared with apple counts made in situ by an agrotechnician, and an R 2 value of 0.86 was acquired (MAE: 10.35 and RMSE: 13.56). As only parts of the tree fruits were visible in the top-view images, linear regression was used to estimate the number of total apples on each tree. An R 2 value of 0.80 (MAE: 128.56 and RMSE: 130.56) was obtained. With the number of fruits detected and tree coordinates two shapefile using Python script in Google Colab were generated. With the previous information two yield maps were displayed: one with information per tree and another with information per tree row. We are confident that these results will help to maximize the crop producers' outputs via optimized orchard management.

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Apple fruit size estimation using a 3D machine vision system

Cited by 77 publications

References 16 publications

Comparison of Different Remote Sensing Methods for 3D Modeling of Small Rock Outcrops

Comparison of Different Remote Sensing Methods for 3D Modeling of Small Rock Outcrops

Size Classification of Tomato Fruit Using Thresholding, Machine Learning, and Deep Learning Techniques

A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique

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