Dear Ahmed Waheed,

Increasing the number of images in the dataset will help the model for a better learning process. However, the crucial part starts with the way of designing your model. Since a model cannot work perfectly for all the datasets, you need to design your system by optimizing its parameters for a specific task of yours. Moreover, to analyze your system performance, I recommend you check other performance metrics, and check the confusion matrix.

For the segmentation part, you could check the following paper in which we extracted several features from the segmented images to feed our model in order to improve the classification performance of our model. According to our results, this approach increased the performance of the classification.

Paper1:

https://www.researchgate.net/publication/343937860_A_Large-Scale_Dataset_for_Fish_Segmentation_and_Classification

Paper2: https://www.researchgate.net/publication/335368522_A_Comparative_Analysis_on_Fruit_Freshness_Classification

Best regards,

Dear Ahmed Waheed ,

You need to change your preprocessing technique, if you want to better accuracy you can segment the disease-affected portion in the preprocessing. You can apply DenseNet-201, M r101 X101*1, Inception ResNet-V3 algorithm, I think this performs very well in your dataset.

You can check this article for better understanding, I think it will help you a lot.

1. Article An Automated Convolutional Neural Network Based Approach for...

2. Article Recognizing Human Emotions from Eyes and Surrounding Feature...

3. Conference Paper Deep Learning Based Classification of Papaya Disease Recognition

As your dataset size is very small, my suggestion is not to use pretrained model if you want better test accuracy, I'm guessing your training accuracy is good and the model is overfitting. Otherwise, go for parameter tuning. May introduce some level of dropout also. By the way, could you tell me what type of segmentation method you've used?

Hasin Rehana We have used k Means clustering.