Try to read some research papers related to your work. If you want to work with fruit disease, then read research papers related to fruit disease recognition, classification, identification. Read carefully about the dataset, implementation process, and result. Hope you will be understood well everything easily.

Try to read some research papers related to your work. If you want to work with fruit disease, then read research papers related to fruit disease recognition, classification, identification. Read carefully about the dataset, implementation process, and result. Hope you will be understood well everything easily.

You use Convolution Neural Network (CNN) with its state of art architectures. For image processing this is the best. You download some pdfs from Google on CNN, you can surely understand..

To running ANN, you need first a relevant data set, including predictive/output values (may be a given disease) and predictors/inputs (variables which are significantly correlated to predictive values). Input values may be continuous or categorical. Then try to train ANN, and validate how to learned. Then you can test ANN potential to predict Occurrence of the disease.

For instance, follow this research:

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CNN is batter option But nowadays research used deep learning it depends on your work you want to check the accuracy or efficiency for that reason read more paper then you get your point.

CNN is a deep learning technique which is the best for image application...

Imdadul Haque Yes, Neural Network is a powerful tool and Deep Convolutional Neural Netowks are very much useful as per your query. You just cannot able to say which network is preferably the best one for your problem, lot of factors are behind that like size of your database, balanced classes or not etc. But what I can suggest is that go for transfer learning, that may give you comparatively better result, that too depends on your configuration for the model.

Convolutional neural networks are the best for disease identification in fruits. You can try segmentation to detect the disease. However there are many possible architectures to do this. For example, You can try a combination of Resnet-Unet. I used it in my course project for segmentation.

Many factors will consider the answer to this question;specificaly,the shortage of data-set(depending on the task purpose means different)should be considered beyond others to make you pick the best classifier.If you have the required data,so Neural Network (convolutional neural network)will be the best option;other consideration is byond the scope of prescription,because many thing on the way to processing an image is experimental and not predefined;as a case in point number of Dense layers,Dim,Epochs etc.

try watershed segmentation

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CNN would more appropriate. All the best