Real-Time Surveillance for Identification of Fruits Ripening Stages and Vegetables Maturation Stages with Infection Detection

Jaspin, K.; Selvan, S.; Rose, J. Dafni; Ebenezer, Jeswin; Chockalingam, Arun

doi:10.1109/ispcc53510.2021.9609441

Cited by 5 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the retrieved local characteristics were used to calculate the locality-preserving projection (LoPP), which was then used as a feature for classification. In order to classify data, a fine tree DTA classifier and a one-againstone multi-class SVM classifier with a 30% holdout were used [11]. 328 locally taken example photos of mangos and oranges, along with 149 images collected from publicly accessible data, were used to assess the effectiveness of the suggested approach.…”

Section: Related Workmentioning

confidence: 99%

Orange Fruit Image Quality Assessment Using Hybrid Model

Ubaid Ullah,

Ali Hussain,

Usman Ali

et al. 2023

JAC

View full text Add to dashboard Cite

Since ripeness is regarded by consumers as the most important quality indication in the crops industry, maintaining and monitoring its ripeness has become a major issue in the business of growing crops. Additionally, as it significantly affects the product's quality and consumer preferences, the product's appearance is one of the makers' most pressing worries. However, the forecasting of storage life and the selection of the best harvest dates are still largely based on individual interpretation and practical experience. Threshold segmentation and certain morphological techniques are used in the early stage of the extraction of an area of interest. The segmented orange images are then divided into training and testing data sets, and the second stage entails extracting color-based traits from them. Choosing the classifier training settings is the focus of the third phase. The final phase, which makes use of the previously trained ANN, categorizes the data. One of the most crucial conclusions of this study is that creating a neural network is an empirical process that requires a lot of trials in order to identify the ideal variables that will give the neural classification algorithm the best performance. initialization of weights, the quantity of hidden neurons. Based on the proposed model the accuracy and efficiency is 1.3% which is more accurate result.

show abstract

Section: Related Workmentioning

confidence: 99%