A Comparative Analysis on Fruit Freshness Classification

Karakaya, Diclehan; Ulucan, Oguzhan; Türkan, Mehmet

doi:10.1109/asyu48272.2019.8946385

Cited by 32 publications

(15 citation statements)

References 16 publications

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“…On Dataset 2, our developed method yields 99.8% classification accuracy and beats all the compared methods. In this dataset, the ResNet-50 based method [24] also reported an encouraging classification accuracy of 98.89%. Moreover, dataset [20] reported detailed classification results by implementing several architectures with the mean outcome of 88.77% fruit freshness classification.…”

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confidence: 74%

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Automatic Fruits Freshness Classification Using CNN and Transfer Learning

Amin

Shahzad

et al. 2023

Applied Sciences

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Fruit Freshness categorization is crucial in the agriculture industry. A system, which precisely assess the fruits’ freshness, is required to save labor costs related to tossing out rotten fruits during the manufacturing stage. A subset of modern machine learning techniques, which are known as Deep Convolution Neural Networks (DCNN), have been used to classify images with success. There have recently been many changed CNN designs that gradually added more layers to achieve better classification accuracy. This study proposes an efficient and accurate fruit freshness classification method. The proposed method has several interconnected steps. After the fruits data is gathered, data is preprocessed using color uniforming, image resizing, augmentation, and image labelling. Later, the AlexNet model is loaded in which we use eight layers, including five convolutional layers and three fully connected layers. Meanwhile, the transfer learning and fine tuning of the CNN is performed. In the final stage, the softmax classifier is used for final classification. Detailed simulations are performed on three publicly available datasets. Our proposed model achieved highly favorable results on all three datasets in which 98.2%, 99.8%, and 99.3%, accuracy is achieved on aforesaid datasets, respectively. In addition, our developed method is also computationally efficient and consumes 8 ms on average to yield the final classification result.

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confidence: 74%

“…In this section, we present the comparison of our developed method with several other methods on same datasets. We compare our work with three recently reported fruit freshness classification methods [5,19,20,[23][24][25]. For fair comparison, we use the same training strategy as reported by above-described methods.…”

Section: Comparisonmentioning

confidence: 99%

Automatic Fruits Freshness Classification Using CNN and Transfer Learning

Amin

Shahzad

et al. 2023

Applied Sciences

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“…So, we conclude the efficiency of the gripper is 60 percent. This percentage can be further increased to 80 percent by modifying the gripper specifications [6].…”

Section: Figure 6 Detection Of Fruitmentioning

confidence: 99%

Robotic Arm for Segregation Using Image Processing

Drashti Parmar,

Sumeet Rajak,

Sourabh Patil

et al. 2024

Int Res J Adv Engg Hub

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The recent surge in industrial expansion can be attributed significantly to the advancements in automation technology. For better performance of industrial processes, automated systems are used. Image processing has played a great role in the applications of robotics and embedded systems. Sorting of objects is usually done by humans which takes a lot of time and effort. By employing image processing methods alongside appropriate sensors, object detection becomes feasible, enabling the utilization of robotic arms for sorting diverse items. This reduces human effort and improves the time to market the product. The model includes the conveyor system, capturing and detecting the objects and placing the objects. Using image processing techniques, the captured image is compared with its texture and color. Based on image processing, the robotic arm will be controlled and place the objects in desired locations. An Arduino model is used to process the captured image. The captured image is other than a pre-specified object, it involves the conveyor belt starting to move and the object is collected at the end of the conveyor system.

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“…Apart from the agricultural and plantation areas already mentioned, there are other areas such as detection of fruit freshness. The problem that gave rise to the idea of applying deep learning to detect fruit freshness is that conventional spoilage detection techniques are still slow and time-consuming [27]. Based on these problems, a method for detecting rotten fruit was developed based on digital image processing with machine learning [28]- [30], which has proven to give high potential in the agricultural and plantations industry [31].…”

Section: Introductionmentioning

confidence: 99%

“…From the high potential generated by the application of machine learning, Karakaya et al conducted a comparative study of machine learning and deep learning feature extraction [27]. The results obtained are deep learning provides better accuracy than machine learning [27]. Then further, Chakraborty et al implemented deep learning called MobileNetV2 to identify rotten fruit [32].…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient deep learning model for fruit freshness detection

Valentino

Cenggoro

Elwirehardja

et al. 2023

IJ-AI

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Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness companies that require AI implementations. Based on these problems, we propose to build a convolutional neural network (CNN) model consisting of six layers to detect fruit freshness and save energy. The CNN model we built uses electrical power ranging from 55 to 73 Watts during the training process and 20 to 27 Watts during the testing process. For accuracy, the result is 98.64%. However, compared to previous studies with the MobileNetV2 model, our model only excels in several aspects, such as recall in fresh banana and fresh oranges, recall and F1-score in Rotten Banana.

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A Comparative Analysis on Fruit Freshness Classification

Cited by 32 publications

References 16 publications

Automatic Fruits Freshness Classification Using CNN and Transfer Learning

Automatic Fruits Freshness Classification Using CNN and Transfer Learning

Robotic Arm for Segregation Using Image Processing

Energy-efficient deep learning model for fruit freshness detection

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