Apple Sweetness Measurement and Fruit Disease Prediction Using Image Processing Techniques Based on Human-Computer Interaction for Industry 4.0

Table 3

Comparative study of existing works.

Rose	When the grey scale picture was thresholded, it was possible to segment the image. The classification of illness was carried out using existing classification technique	Qayyum et al. [46]
Cotton	The extraction of texture, colour, and form features from images was explored from the standpoint of plant disease identification.	Y. Song et al. [47]
Cucumber	To reduce the amount of noise in the collected photos, the vector median filtering approach was used.; To segment the picture, a statistical pattern recognition approach and a mathematical morphology methodology were used. Cucumber sickness was identified with the use of statistical learning models (SVM).	Y. Tian et al. [48]
Sugarcane	Color, shape, and texture were retrieved from the picture as distinct characteristics. The characteristics that were collected were then utilised to classify the data using a support vector machine classifier, which is a machine learning algorithm.	R. K. Dewi and R. V. H. Ginardi [49]
Apple	An uneven second modification approach using the chlorophyll captivation waveband, as well as two bands in the near infrared region, was employed to give the identification of faulty sections of apples that were independent of the apple shade and cultivar used in the experiment.	J. Feng et al. [50]