Convolutional Neural Network for Pineapple Ripeness Classification Machine

Chaikaew, Anusorn; Thanavanich, Thanawut; Duangtang, Pumipong; Sriwanna, Kittakorn; Jaikhang, Wipobh

doi:10.1109/ecti-con47248.2019.8955408

Cited by 14 publications

(8 citation statements)

References 3 publications

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“…Color feature and shape of the grapes [8] were chosen as features for classification where CNN model showed 79.49% accuracy, whereas SVM showed 68% classification accuracy, concluding that CNN is better for image classification than SVM. Similarly, in [9], CNN is used to classify the ripeness of the pineapple fruit. Color is used as features for classification [9].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, in [9], CNN is used to classify the ripeness of the pineapple fruit. Color is used as features for classification [9]. In this research, image data are collected by taking pictures through 3 webcams tricked from Arduino Controller and labeled as unripe, partially ripe and fully ripe.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this research, image data are collected by taking pictures through 3 webcams tricked from Arduino Controller and labeled as unripe, partially ripe and fully ripe. This research has used already trained MobileNet CNN architecture and has predicted 98.38% and 90.77% of training and test accuracy [9].…”

Section: Literature Reviewmentioning

confidence: 99%

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Automated Agronomy: Evaluation of Fruits Ripeness Using Machine Learning Approach

Chmaj

Sharma

Selvaraj

2021

Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020

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Fruit orchards require a lot of tasks, monitoring the current state of fruits and anticipated time of harvest. Precise estimation of harvest time is a key knowledge for the supply chain and timely delivery to grocery stores. The current process relies on human agronomists, who visit orchards often and do the visual evaluation of number of fruits, their ripeness and expected time of harvest. In this paper, we propose a preliminary work for automation of the fruits' evaluation process, using machine learning algorithms to evaluate pictures of the trees. For our current system we have used picturesone picture per tree, to constitute the base for evaluation of each tree using multiple pictures and video captured by drones. In this paper, Convolutional Neural Network (CNN) is used for fruit image classification based on ripeness stage of the fruits. Fruits classification was based on appropriate surface color and shape features and CNN was used to extract these features for classification. The model showed 96.43% accuracy. The ultimate goal of this work is to fully automate the process of orchard trees evaluation, including estimation of number of fruits on the tree (including non-visible ones), their ripeness and time of harvest to address the commercial market delivery planning requirements.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Automated Agronomy: Evaluation of Fruits Ripeness Using Machine Learning Approach

Chmaj

Sharma

Selvaraj

2021

Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020

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“…Several researchers have studied how to classify the maturity of agricultural products such as cacao [3], pineapple [1], pineapple ripeness [4], durian ripeness [5], and described optimal pineapple harvesting [6]. Furthermore, researchers have adapted the structure of pre-trained convolutional neural networks (CNNs), using transfer learning, to pre-train AlexNet and VGGNet networks for apple mealiness detection [7].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Sensing for Quality Edible Evaluation of Sriracha Pineapple Using Convolutional Neural Network

Pongpinigpinyo

Phawiakkharakun

Taeprasartsit

2021

Curr. Appl. Sci. Technol.

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Sriracha pineapple is a pineapple of the Smooth Cayenne variety and is one of Thailand's favorite tropical fruits. It is not only one of the most favorite agricultural products for the pineapple processing and canning industries but it is also widely consumed as a fresh fruit. Many exporters and consumers prefer to measure juiciness before processing food products and consuming, respectively. The traditional method used by pineapple sellers or farmers for fruit juiciness classification is to tap the pineapple using force impulse techniques with a rubber-tipped stick or a person's middle finger tapping. However, these traditional methods of classification require a lot of expertise and experience. Thus, the inspector's perception of accomplishing the classification process may be inclined to errors and uneven results. This paper proposes a combination of acoustic sensing and convolutional neural network (CNN). The tapping sound of 30 Sriracha pineapple samples using force impulse techniques was recorded on smartphone. The tapping sounds were processed into a juiciness classification system of three classes: (1) Juiciness 1, (2) Juiciness 2, and (3) Juiciness 3. The system involved a combination of acoustic sensing and CNN to compare the results between Mel Frequency Cepstral Coefficient (MFCC) and Mel-Spectrogram features extraction, with replication of the same CNN model, to evaluate the pineapple's edibility from its juiciness level. Experimental results showed that MFCC combined with CNN performed the best, with an accuracy of 96.67% and F1-score of 0.97. It outperformed the Mel-spectrogram combined with CNN.

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“…Aside from these methods, integration of deep neural networks like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) also gained popularity in areas of computer vision, and audio processing wherein captured signals are passed through acoustic spectrogram transformations, and then classified based on extracted spectrogram features. For instance, studies on ripeness estimation of grapes [22], banana [23], pineapple [24], durian [25], coffee beans [26], and other artificially ripened fruits [27] have utilized the potential of CNN in carrying out high accuracy classification. Other useful studies on the manipulation of CNN modeling are shown in other applications like in [28]- [30], while commonly used machine learning like SVM, Decision Tree, and Naïve Bayes can also be used in training the data [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Maturity classification of cacao through spectrogram and convolutional neural network

Bueno

Valenzuela

Arboleda

2020

Jurnal Teknologi dan Sistem Komputer

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Cacao pod's ideal harvesting time is when it is about to be ripe. Immature harvest would result in hard cacao beans not suitable for fermentation, while overripe cacao pods lead to fungal-infected, defective, and poor-quality yields. The demand for high-quality cacao products is expected to rise due to advancing technology in the present. Pre-harvesting needs to provide optimal identification of which amongst the pods are ripened enough and ready for the next stage of the cacao process. This paper recommends a technique to determine the ripeness of cacao. Nine hundred thirty-three cacao samples were used to collect thumping audio data at five different pod's exocarp locations. Each sound file is 1 second long, creating 4665 cacao sound file datasets at 16kHz sample rate and 16-bit audio bit depth. The process of the Mel-Frequency Cepstral Coefficient Spectogram was then applied to extract recognizable features for the training process. The deep learning method integrated was a convolutional neural network (CNN) to classify the cacao sound successfully. The experimental design model's output exhibits an accuracy of 97.50 % for the training data and 97.13 % for the validation data. While the overall accuracy mean of the classification system is 97.46 %, whether the cacao is unripe or ripe.

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Convolutional Neural Network for Pineapple Ripeness Classification Machine

Cited by 14 publications

References 3 publications

Automated Agronomy: Evaluation of Fruits Ripeness Using Machine Learning Approach

Automated Agronomy: Evaluation of Fruits Ripeness Using Machine Learning Approach

Acoustic Sensing for Quality Edible Evaluation of Sriracha Pineapple Using Convolutional Neural Network

Maturity classification of cacao through spectrogram and convolutional neural network

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