Abstract:
Due to a variety of crop species, crop diseases, and environmental conditions, early
disease detection is the most difficult task. Several machine learning approaches have
been developed to make this challenge easier. Data was primarily gathered by
researchers to build their model. They gathered data in a variety of ways, including
manually gathering data, downloading data from Google, and obtaining ready-made
data from third parties. Because they used a variety of strategies, they received
varying accuracy percentages. Even though everyone tried their best to reach the
utmost accuracy, no one could come up with the same outcome. In order to construct
my model, I employed CNN architecture. I gathered information for this from the
Kaggle dataset. Since Kaggle is open source, researchers may quickly gather the
precise data they need. After putting my model to the test, I got 99% accuracy.
Disease detection from the leaves is very difficult. Early Blight and Late Blight are
prevalent diseases in potato leaves. Those who are identified too late harm the crop.
For this farmer must deal for both money loss and potato waste.
