A Study of Deep Learning Method Opportunity on Palm Oil FFB (Fresh Fruit Bunch) Grading Methods

Aji, Wahyu Sapto; Hawari, Kamarul

doi:10.2991/adics-es-19.2019.9

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Artificial Neural Networks [ANN] is integrated for analysis and achieved accuracy of 70% [9]. Advanced artificial intelligence tools such as machine learning or deep learning are also used [5,[11][12][13][14][15][16]. Images are collected, pre-processed and trained under the artificial neural network (ANN), and the ANN learns the features from those images [9,18].…”

Section: Related Workmentioning

confidence: 99%

Novel Feature Extraction for Oil Palm Bunches Classification

Hui Hui Wang,

Yin Chai Wang,

Bui Lin Wee

et al. 2023

ARASET

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This paper presents research on an image processing approach for oil palm fruit brunches classification on automating the process of classifying palm fruit brunches based on their visual characteristics, which can have applications in the agriculture and palm oil industry. The research aims to classify the bunches into four categories which are unripe, under ripe, ripe and over ripe. Additionally, this approach can reduce the manual labour involved in palm oil grading and provide a more objective and consistent method for grading the oil. The proposed approach consists of the process of image acquisition, image pre-processing, colour processing, image segmentation and classification. The ripeness of the oil palm fruit bunches is determined based on the percentage of ripeness areas masked on the fruit surface. The proposed algorithms successfully classified the palm oil bunches and improved the accuracy of grading palm oil, achieving 85% accuracy from the experiment results.

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Section: Related Workmentioning

confidence: 99%

Novel Feature Extraction for Oil Palm Bunches Classification

Hui Hui Wang,

Yin Chai Wang,

Bui Lin Wee

et al. 2023

ARASET

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“…Another use of deep learning in agriculture is classification. Models have been used for automatic fruit and vegetable classification, allowing faster and more accurate classification according to quality and ripeness (Aji et al, 2019). Deep learning models can enable robots and drones to perform tasks such as autonomous harvesting, crop monitoring, and autonomous spraying (Ampatzidis et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

YOLOv5 Model Application in Real-Time Robotic Eggplant Harvesting

Kahya,

Ozduven,

Aslan

2024

JAS

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Deep learning studies in agricultural automation have accelerated in recent years due to its benefits such as increasing product efficiency and reducing labor force. Deep learning is a powerful tool for automation in agriculture with applications ranging from disease identification and crop yield detection to fruit ripeness classification. It helps to automate various processes in agriculture and to perform time-consuming tasks in a shorter time. It quickly processes the data required for robotic harvesting systems and makes it available to the system. In this study, a machine learning study was carried out to be used in the robotic harvesting of eggplant fruit, which is a product that can take time to select and collect in the agricultural area where it is cultivated. YOLOv5 (nano-small-medium and large models) was used for the deep learning method. All training and test metric values of the models were analyzed. It was determined that the most successful model was the model trained with YOLOv5m algorithm on images of 640 × 640 size with 12 Batches and 110 Epochs. The results of the model values were analyzed as “metrics/precision”, “metrics/recall”, “metrics/mAP_0.5” and “metrics/mAP_0.5:0.95”. These are key metrics that measure the detection success of a model and indicate the performance of the relevant model on the verification dataset. It was determined that the metric data of the “YOLOv5 medium” model was higher compared to other models. The YOLOv5m model gave the highest score with F1 score of 85.66%, precision of 95.65%, recall of 96.15%, and mAP at 0.5:0.65 of 78.80%. Hence, it was understood that “Model 3” was the best detection model to be used in robotic eggplant harvesting to separate the eggplant from branch.

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Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Yang,

Zhou,

et al. 2024

Plant Methods

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Background Camellia oleifera, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types. Methods This study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of C.oleifera, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation R2 and and time consumption. Results When using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum R2 improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of C. oleifera, providing a valuable tool for predicting the impact of grafting combinations on the fruit of C. oleifera.

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A Study of Deep Learning Method Opportunity on Palm Oil FFB (Fresh Fruit Bunch) Grading Methods

Cited by 4 publications

References 18 publications

Novel Feature Extraction for Oil Palm Bunches Classification

Novel Feature Extraction for Oil Palm Bunches Classification

YOLOv5 Model Application in Real-Time Robotic Eggplant Harvesting

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

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