Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.
For the most part, mango leaves principally get influenced by three basic sicknesses they are Anthracnose, Bacterial canker, Powdery mildew. The previously mentioned infections influence the development of mango tree, decline the life expectancy and decrease the natural product creation. Thinking about this we were doing the task under the territory of leaf malady characterization. The fundamental point of our venture is to arrange the state of the leaf, needs to recognize the malady from which it is experiencing. Thus, it will be for the most part helpful for ranchers keeping from crop misfortune financially. They can kill the illness in the underlying state itself. We were utilizing the profound learning system to recognize the tainted leaves. We propose a CNN single-stream model to order the picture. Our informational collection comprises a sum of 800 pictures arranged into two kinds, the main sort comprises a preparation set and the subsequent kind comprises of the testing set. Preparing comprises of 150 pictures and testing comprises of 50 pictures in every envelope.
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