Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Efficient detection of ‘species and variety’ of fruits and vegetables from the images is one of the major challenges for the computers. ,  introduce a framework for the fruit and vegetable classification problem which takes the images of fruits and vegetables as input and returns it is species and variety. The input image contains fruit or vegetable of single variety in arbitrary position and in any number.  also introduces a texture feature based on sum and difference of intensity values of the neighbouring pixels of the colour images. The experimental results show that the proposed texture feature supports accurate fruit and vegetable recognition and performs better than other state-of-the-art colour and texture features. The classification accuracy for the proposed ISADH texture feature is achieved up to 99%.
['Shiv Ram Dubey', 'Anand Singh Jalal']
I am writing a research paper on a idea Species and variety detection of fruits and vegetables from images
[{"from": "human", "value": "I am writing a research paper on a idea Species and variety detection of fruits and vegetables from images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Efficient detection of \u2018species and variety\u2019 of fruits and vegetables from the images is one of the major challenges for the computers. ,  introduce a framework for the fruit and vegetable classification problem which takes the images of fruits and vegetables as input and returns it is species and variety. The input image contains fruit or vegetable of single variety in arbitrary position and in any number.  also introduces a texture feature based on sum and difference of intensity values of the neighbouring pixels of the colour images. The experimental results show that the proposed texture feature supports accurate fruit and vegetable recognition and performs better than other state-of-the-art colour and texture features. The classification accuracy for the proposed ISADH texture feature is achieved up to 99%."}]