Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning

Zhou, Xue; Lee, Won Suk; Ampatzidis, Yiannis; Chen, Yang; Peres, Natália A.; Fraisse, Clyde W.

doi:10.1016/j.atech.2021.100001

Cited by 45 publications

(23 citation statements)

References 19 publications

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“…Potentially, future studies could also explore the use of unmanned aerial vehicles (drones) with articial intelligence systems to monitor farms for pawpaw fruit skin color parameters to ensure prompt harvesting of fruits to prevent postharvest losses as has been done for fruits like strawberries and apples. 28,29 Since it has already been established that the textural properties like hardness and cohesiveness ratio of pawpaw fruits are better indicators of ripeness, Pearson's correlation analysis was performed to test the relationship between the color parameters and the textural properties to evaluate which color parameters correlate with the textural properties and may be used as indicators of ripeness. Studies on climacteric fruits like apples and bananas show that specic color parameters like a* values, b* values, and hue angles can be used as indicators of ripeness.…”

Section: Correlation Analysis Of Measured Quality Characteristicsmentioning

confidence: 99%

Correlations between color, textural properties and ripening of the North American pawpaw (Asimina triloba) fruit

Adainoo

Thomas

Krishnaswamy

2023

Sustainable Food Technol.

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Section: Correlation Analysis Of Measured Quality Characteristicsmentioning

confidence: 99%

Correlations between color, textural properties and ripening of the North American pawpaw (Asimina triloba) fruit

Adainoo

Thomas

Krishnaswamy

2023

Sustainable Food Technol.

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“… Chen et al (2019) used a UAV to capture images of the strawberry crop, and then utilized Faster-R-CNN to detect strawberry flowers and immature and mature strawberries with 84.1% accuracy. Zhou et al (2021) also divided the growth of strawberries into three stages, “flowers,” “immature fruits,” and “mature fruits,” and utilized the YOLOv3 model to detect images photographed with a UAV. The experimental results show that the model has the best detection effect on the data set taken with the UAV 2 m away from fruits, and the mAP reaches 0.88.…”

Section: Convolutional Neural Network-based Fresh Fruit Detectionmentioning

confidence: 99%

“…Fruits photographed with the UAV equipment are too small because of long distance, and the characteristics are relatively fuzzy. In Zhou et al (2021) , researchers used UAV equipment and a handheld camera equipment for data acquisition. They divided the strawberry data captured with the camera into seven different growth stages: flower fruits, green fruits, green-white fruits, white-red fruits, red fruits, and rotten fruits.…”

Section: Convolutional Neural Network-based Fresh Fruit Detectionmentioning

confidence: 99%

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

et al. 2022

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As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantage of local perception and parameter sharing has been rapidly developed. CNN-based detection technology has been widely used in computer vision, natural language processing, and other fields. Fresh fruit production is an important socioeconomic activity, where CNN-based deep learning detection technology has been successfully applied to its important links. To the best of our knowledge, this review is the first on the whole production process of fresh fruit. We first introduced the network architecture and implementation principle of CNN and described the training process of a CNN-based deep learning model in detail. A large number of articles were investigated, which have made breakthroughs in response to challenges using CNN-based deep learning detection technology in important links of fresh fruit production including fruit flower detection, fruit detection, fruit harvesting, and fruit grading. Object detection based on CNN deep learning was elaborated from data acquisition to model training, and different detection methods based on CNN deep learning were compared in each link of the fresh fruit production. The investigation results of this review show that improved CNN deep learning models can give full play to detection potential by combining with the characteristics of each link of fruit production. The investigation results also imply that CNN-based detection may penetrate the challenges created by environmental issues, new area exploration, and multiple task execution of fresh fruit production in the future.

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“…In some recent studies, Zhou et al (2021) [28] collected drone-based high-resolution RGB images and used a YOLOv3 deep learning algorithm to classify strawberries in three and seven ripeness stages. The training data were prepared by manually Labeling the ripeness stages of strawberries.…”

Section: Introductionmentioning

confidence: 99%

Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing

2022

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We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the oven-dry method. To estimate the water content, 674 and 698 nm bands were selected to create a normalized difference strawberry water content index. The index was used as an input to a logarithmic model for estimating fruit water content. The model for water content estimation gave a correlation coefficient of 0.82 and Root Mean Squared Error (RMSE) of 0.0092 g/g. For ripeness classification, a Support Vector Machine (SVM) model using the full spectrum as input achieved over 98% accuracy. Our analysis further show that, in the absence of the full spectrum data, using our proposed water content index as input, which uses reflectance values from only two frequency bands, achieved 71% ripeness classification accuracy, which might be adequate for certain applications with limited sensing resources.

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Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning

Cited by 45 publications

References 19 publications

Correlations between color, textural properties and ripening of the North American pawpaw (Asimina triloba) fruit

Correlations between color, textural properties and ripening of the North American pawpaw (Asimina triloba) fruit

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing

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