Enhanced CNN for Fruit Disease Detection and Grading Classification Using SSDAE-SVM for Postharvest Fruits

In the realm of agriculture, leveraging image processing has become pivotal for robust image analysis, especially in detecting fruit diseases. However, existing techniques in this domain often limit inputs to fixed sizes without reshaping images before neural network (NN) input, complicating disease...

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Veröffentlicht in:	IEEE sensors journal Jg. 24; H. 5; S. 6719 - 6732
Hauptverfasser:	Patel, Himanshu B., Patil, Nitin J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Backpropagation (BP) with adaptive momentum Convolutional neural networks Disease Diseases enhanced convolutional neural network (CNN) with spatial pyramid pooling (SPP) Feature extraction FIR filters Image analysis Image color analysis Image processing Image resolution Medical imaging optimum finite impulse response (FIR) Wiener filter Prediction models Production Radio frequency spatial bin stacked sparse denoising autoencoder (SSDAE) support vector machine (SVM) Support vector machines Surface layers
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
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