Yuchan Chen
ABSTRACT
Analysis and identification of watermelon ripeness is a very important link in agricultural production and melon and fruit sales, which can greatly improve the market circulation efficiency of watermelon. In this paper, watermelon ripeness discrimination was studied based on back propagation neural network (BPNN), watermelon acoustic modeling and svm classification model. In this project, mechanical acquisition equipment also was made. The reasonable circuit structure was designed to drive the automatic arm whipping machine based on Arduino control board, so as to beat watermelon and generate sound signals. 4 microphone sensors were used to capture audio information and send it to Matlab port through USB serial port for saving and preprocessing sound data. Finally, the comprehensive recognition rate reached 90.325%, which can accurately identify watermelon individuals corresponding to each signal, so as to provide scientific reference for the production and circulation of fruits and vegetables.
- Dengfei Jie, Wanhuai Zhou, Xuan Wei. Nondestructive detection of maturity of watermelon by spectral characteristic using NIR diffuse transmittance techniqueGoogle Scholar
- N. Ahmad, Syazwan, M.S.B. Shah Rizam, M.T. Nooritawati. Categorization Of Watermelon Maturity Level Based On Rind FeaturesGoogle Scholar
- Xuan Chen, Peipei Yuan, Xiaoyan Deng. Watermelon ripeness detection by wavelet multiresolution decomposition of acoustic impulse response signalsGoogle Scholar
- Rouzbeh Abbaszadeh, Ali Rajabipour, Hassan Sadrnia, Mohammad J. Mahjoob Mojtaba Delshad Hojjat Ahmadi. Application of modal analysis to the watermelon through finite element modeling for use in ripeness assessmentGoogle Scholar
- Kehinde Abraham ODELADE, Oluwole Solomon OLADEJI. Isolation of phytopathogenic fungi associated with the post-harvest deterioration of watermelon fruitsGoogle Scholar
- Pengzhan Jin, Lu Lu, Yifa Tang, George Em Karniadakis. Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothnessGoogle Scholar
- Chen L, Zhou M, Su W, Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction[J]. Information Sciences, 2018, 428: 49-61. Google ScholarDigital Library
- Zangeneh E, Rahmati M, Mohsenzadeh Y. Low resolution face recognition using a two-branch deep convolutional neural network architecture[J]. Expert Systems with Applications, 2020, 139: 112854.Google ScholarDigital Library
Index Terms
- Research on Classification of Watermelon Ripeness Based on Neural Network Pattern Recognition
Recommendations
Classifying watermelon ripeness by analysing acoustic signals using mobile devices
This work addresses the problem of distinguishing between ripe and unripe watermelons using mobile devices. Through analysing ripeness-related features extracted by thumping watermelons, collecting acoustic signals by microphones on mobile devices, our ...
An IPSO-BP neural network for estimating wheat yield using two remotely sensed variables in the Guanzhong Plain, PR China
Highlights- Simple NN models are suitable at integrating data measuring the condition of wheat.
AbstractEarly and accurate information of crop growth condition is vital for agricultural industry and food security, which gives rise to a strong demand for timely monitoring crop growth condition and estimating crop yields. This study ...
Research on site classification method based on BP neural network
EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer EngineeringIn order to improve the accuracy of large-scale site classification, the BP neural network method is used to study the site classification. According to the existing research, there is a certain correlation between site type and slope, elevation and ...
Comments