Comparison of Various Classifiers for Efficient Plant Diseases Detection Using Image Processing Techniques

Processing images of the fruit, the leaves, or the entire plant, image processing enables the early diagnosis of fruit illnesses. In order to stop the spread of illnesses and reduce crop losses, early diagnosis is essential. Image processing techniques can identify subtle changes in color, texture, and shape that are often indicative of disease symptoms. This level of precision and accuracy is challenging to achieve through human visual inspection alone. Image processing provides an objective and consistent analysis of disease symptoms. Human judgment can be influenced b y various factors, whereas i mage processing algorithms follow predefined criteria, reducing s ubjectivity, To create predictive models, image processing may be combined with other technologies like machine learning and artificial intelligence (AI). Based on past data and environmental factors, these models can predict disease outbreaks. In this work we are discussing various existing image processing techniques that will predict the diseases in fruit efficiently and accurately.