Recognition of ripe, unripe and scaled condition of orange citrus based on decision tree classification

Wajid, Abdul; Singh, Niraj Kumar; Pan, Junjun; Mughal, Muhammad Ali

doi:10.1109/icomet.2018.8346354

Cited by 33 publications

(18 citation statements)

References 6 publications

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“…In recent years, relevant scholars have carried out a series of research on recognizing and detecting fruits, such as apples and citrus in precision orchards ( Liao et al, 2017 ; Wajid et al, 2018 ; Gurubelli et al, 2019 ; Mo et al, 2021 ). Lin G. et al (2020) adopted partial shape matching and probabilistic Hough transform to detect fruits in the natural environment.…”

Section: Introductionmentioning

confidence: 99%

Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model

et al. 2022

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The precision detection of dense small targets in orchards is critical for the visual perception of agricultural picking robots. At present, the visual detection algorithms for plums still have a poor recognition effect due to the characteristics of small plum shapes and dense growth. Thus, this paper proposed a lightweight model based on the improved You Only Look Once version 4 (YOLOv4) to detect dense plums in orchards. First, we employed a data augmentation method based on category balance to alleviate the imbalance in the number of plums of different maturity levels and insufficient data quantity. Second, we abandoned Center and Scale Prediction Darknet53 (CSPDarknet53) and chose a lighter MobilenetV3 on selecting backbone feature extraction networks. In the feature fusion stage, we used depthwise separable convolution (DSC) instead of standard convolution to achieve the purpose of reducing model parameters. To solve the insufficient feature extraction problem of dense targets, this model achieved fine-grained detection by introducing a 152 × 152 feature layer. The Focal loss and complete intersection over union (CIOU) loss were joined to balance the contribution of hard-to-classify and easy-to-classify samples to the total loss. Then, the improved model was trained through transfer learning at different stages. Finally, several groups of detection experiments were designed to evaluate the performance of the improved model. The results showed that the improved YOLOv4 model had the best mean average precision (mAP) performance than YOLOv4, YOLOv4-tiny, and MobileNet-Single Shot Multibox Detector (MobileNet-SSD). Compared with some results from the YOLOv4 model, the model size of the improved model is compressed by 77.85%, the parameters are only 17.92% of the original model parameters, and the detection speed is accelerated by 112%. In addition, the influence of the automatic data balance algorithm on the accuracy of the model and the detection effect of the improved model under different illumination angles, different intensity levels, and different types of occlusions were discussed in this paper. It is indicated that the improved detection model has strong robustness and high accuracy under the real natural environment, which can provide data reference for the subsequent orchard yield estimation and engineering applications of robot picking work.

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Section: Introductionmentioning

confidence: 99%

Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model

et al. 2022

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“…A summary of recent fruit and vegetable classification performed in different real-life applications is presented in Table 1. Recent state-of-the-art for fruit and vegetable classification and recognition are a combination of feature description and machine learning algorithms on visual data [1,12,13,14,15]. Significant research has been reported for representation of different characteristics of fruit and vegetable as feature vectors [6,16].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

155

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Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-ofthe-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable.

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“…There are several weaknesses in the manual method. It takes a relatively long time to do it, requires a lot of labor, and can cause inconsistencies in determining fruit ripeness [5], [6], [7]. The emergence of computer vision technology can overcome these problems because the classification of fruit ripeness can be done automatically; therefore, it is relatively quick, consistently, and relatively inexpensive [2].…”

Section: Table Of Contentmentioning

confidence: 99%

Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network

Saragih

Emanuel

2021

2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT)

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Fruit ripeness is an important thing in agriculture because it determines the fruit's quality. Determining the ripeness of the fruit that was done manually poses several weaknesses, such as takes a relatively long time, requires a lot of labor, and can cause inconsistencies. The agricultural sector is one of the important sectors of the economy in Indonesia. However, sometimes the process of determining fruit ripeness is still done by using the manual method. The development of computer vision and machine learning technologies can be used to classify fruit ripeness automatically. This study applies the Convolutional Neural Network to classify the ripeness of the banana. The banana's ripeness is divided into four classes: unripe/green, yellowish-green, mid-ripen, and overripe. Two pre-trained models are used, which are MobileNet V2 and NASNetMobile. The experiment was conducted using Google Colab and several libraries such as OpenCV, Tensorflow, and scikit-learn. The result shows that MobileNet V2 achieves higher accuracy and faster execution time than the NASNetMobile. The highest accuracy achieved is 96.18%.

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Recognition of ripe, unripe and scaled condition of orange citrus based on decision tree classification

Cited by 33 publications

References 6 publications

Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model

Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model

A comprehensive review of fruit and vegetable classification techniques

Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network

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