Pomegranate is an important crop that is widely cultivated for its juicy and nutritious fruit. However, this
fruit is susceptible to various diseases that can cause significant losses to the grower. Therefore, it is
crucial to detect the diseases at an early stage to prevent the spread of the disease and reduce the
A semantic segmentation model for pomegranate disease detection works by training the model on a
large dataset of pomegranate images with and without diseases. The model is then able to identify the
infected regions of the fruit and predict the type of disease like bacterial blight, anthracnose, pest attack,
cercospora fruit spot , scab etc.
This trained model can be used to analyze new images and accurately detect the presence and type of
disease. The model offers several advantages over traditional disease detection methods, such as human
inspection and chemical analysis.
-Semantic segmentation models are highly accurate, reducing the risk of false positives and negatives,
leading to more reliable results.
-The use of computer vision techniques enables the analysis of large volumes of images in a short period
of time, making it an efficient solution for disease detection in large pomegranate orchards.
-The use of semantic segmentation models is a non-invasive method that does not require the use of
harmful chemicals, making it an environmentally friendly solution.