Banana Ripeness Classification Based On Image Processing With Machine Learning

Wankhade, Mayuri; Hore, Umesh W.

doi:10.48175/ijarsct-1571

Cited by 5 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, a 4-layer CNN model is trained by Ahmed et al [28] to classify the ripened banana fruits with the highest accuracy. Mayuri et al [29] have designed an automatic system to identify the ripening stages of banana fruit from images. The designed system extracts the features using the Inception V3 model and classification is done by using SVM.…”

Section: Ripening Stages Of Banana Fruitsmentioning

confidence: 99%

A Convolution Neural Network with Skip Connections (CNNSC) approach for detecting micronutrients boron and iron deficiency in banana leaves

Sunitha,

Uma,

Geetha Kiran

et al. 2024

J. Umm Al-Qura Univ. Eng.Archit.

View full text Add to dashboard Cite

Banana plants require a precise balance of 16 essential nutrients to flourish, with micronutrients playing a pivotal role in smaller proportions than macronutrients. Insufficient micronutrient provision in Banana plants can impede growth, hinder flowering, and diminish fruit production. Precise identification of deficiencies is imperative for farmers aiming to cultivate nutrient-dense crops and enhance their yields. Micronutrients such as Boron contribute to hormone regulation, while Iron facilitates enzyme function, DNA synthesis, and overall metabolic processes in plants. Observable manifestations of Boron and Iron deficiencies on banana leaves are crucial indicators necessitating intervention. The present work proposes a novel deep learning methodology, employing a CNN model with Skip Connections (CNNSC), to detect Boron and Iron deficiencies in banana foliage. The CNNSC model, featuring thirteen layers, surpasses established architectures like VGG16, DenseNet, and Inception V3. Training the model on a specialized curated dataset comprising 11,000 nutrient-deficient images, with a split of 70% for training and 30% for testing, has yielded promising outcomes. Key performance metrics, encompassing accuracy, loss, precision, F1-score, recall, and the confusion matrix underscore the efficacy of the model, achieving an impressive accuracy rate of approximately 95%.

show abstract

Section: Ripening Stages Of Banana Fruitsmentioning

confidence: 99%

A Convolution Neural Network with Skip Connections (CNNSC) approach for detecting micronutrients boron and iron deficiency in banana leaves

Sunitha,

Uma,

Geetha Kiran

et al. 2024

J. Umm Al-Qura Univ. Eng.Archit.

View full text Add to dashboard Cite

show abstract

“…There is a vast recent literature on identifying social media fake accounts using machinelearning methods (Kerrysa & Utami, 2023;Goyal et al, 2023;Barde & Wankhade, 2023;Abilash & Sujitha 2023). These methods, even if they use a simple algorithm or a combination of algorithms (for example, convolutional neural network and an artificial neural networks model or support vector machine with an artificial neural networks model model) proved per-formance rates, for Facebook, between 79 and 99.28% (Kerrysa & Utami, 2023, p. 3795).…”

Section: Introductionmentioning

confidence: 99%

Use of Facebook Accounts with Inauthentic Behavior in Elections: The Romanian Presidential Election Case

Oprea

2024

Ro. J. Comm. & PR

View full text Add to dashboard Cite

Astroturfing, trolling, bots, false amplifiers and social media accounts with inauthentic behavior are used in online political communication even if they have a real-world dangerous effect on democratic systems. Some of these activities are involving users that are over-proportionally active in relation to the mean. This explanatory research sought to determine whether such hyperactive users are utilized to share political posts to create an impression of popularity for a specific political message and, thus, to influence the recommendation algorithms of social networks in order to increase the exposure of political messages. In this study, I analyzed the most shared posts during the election campaign on the official Facebook pages of the first three ranked candidates in the 2019 Romanian presidential election. The research revealed an average of 18.3% of shares were made by hyperactive users on their own timeline or in different Facebook groups, with users that shared the same post for 69 times. Furthermore, I identified some of the characteristics of hyperactive users’ accounts based on their public social media profile, which may helps understand the specifics of these accounts. The results show that election communication involves activities considered by Facebook to be a practice of „manipulating public opinion“ and of „manipulating political discussion“ (Weedon et al., 2017, p. 5).

show abstract

“…Furthermore, Wankhade and Hore [14] classify the ripeness of bananas using the SVM method based on feature extraction obtained from the inception v3 model. His research got an accuracy rate of 88.9%.…”

Section: Introductionmentioning

confidence: 99%

Classification of six banana ripeness levels based on statistical features on machine learning approach

Kosasih,

Sudaryanto,

Fahrurozi

2023

IJAAS

View full text Add to dashboard Cite

<span>Banana plants are often cultivated because they have many benefits. In producing, we need to maintain the quality of bananas by looking at banana ripeness levels before being distributed to markets. The level of banana ripeness is related to marketing reach. If the marketing reach is far, bananas should be harvested when the ripeness level of bananas is still relatively low. A system that can classify the degree of ripeness of bananas can help overcome this problem. In this study, our dataset includes 6 ripeness levels of bananas, more than in previous related studies. Furthermore, we use the statistical features extraction method to find the parameters that affect the level of banana ripeness, considering the texture and color of the banana peel which determines the level of ripeness visually. The extraction used is features extraction based on a histogram, then we employ four features, i.e., mean, skewness, energy descriptor, and smoothness, generated from the image dataset. In the next stage, we perform classification based on the features that have been obtained. In this study, we use Naive Bayes classifier and support vector machine (SVM) algorithms. Based on the result of this research, the best performance is the Naive Bayes classifier, with an accuracy is 86.67%, a weighted average precision of 83.55%, and a weighted average recall of 86.67%.</span>

show abstract

Banana Ripeness Classification Based On Image Processing With Machine Learning

Cited by 5 publications

References 13 publications

A Convolution Neural Network with Skip Connections (CNNSC) approach for detecting micronutrients boron and iron deficiency in banana leaves

A Convolution Neural Network with Skip Connections (CNNSC) approach for detecting micronutrients boron and iron deficiency in banana leaves

Use of Facebook Accounts with Inauthentic Behavior in Elections: The Romanian Presidential Election Case

Classification of six banana ripeness levels based on statistical features on machine learning approach

Contact Info

Product

Resources

About