Banana Ripeness Detection Using CNN and Flask

Banana Ripeness Detection Using CNN and Flask

In this blog, we are going to build a Banana Ripeness Detector using Machine learning and for testing, we will also create a flask app.

Bananas are one of the most consumed fruits globally. It contributes about 16% of the world’s fruit production according to FAO( Food and Agriculture Organization ). Almost every third person in the world eats the banana. Banana is a great source of energy, it provides fullness in the body. In every sport, banana is given as an energy booster for players. Eating an overripe banana can be caused you a series of diseases, problems with the stomach, and more. For the last 10 years, people are trying to find the correct ripening stage of banana. The ripening stage of fresh banana fruit is a principal factor that affects the fruit quality during ripening and marketability after ripening, it is also very helpful in increasing the production. Finding the correct ripening stage of banana can be tougher if we know that we produce an average of 100tons of banana every day. we need some intelligent systems that can detect the correct stage of banana.

In this blog, we are going to build a Banana Ripeness Detector using Machine learning and for testing, we will also create a flask app.

Let’s start, First, you need to install some dependencies.

  1. Tensorflow
pip install tensorflow

if you have GPU in your system then go for TensorFlow-GPU with Cuda. you can read my blog for installing Cuda in windows with TensorFlow.

2. Flask

pip install flask

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