Plant Disease Detection Using Machine Learning Techniques

Aim:

           We proposed a complete systematic approach to detect Plant disease using Machine Learning algorithm.

 Abstract:

        This paper presents a survey on methods that use digital image processing techniques to detect plant leaf diseases, there’s a variety of development which has been made regarding image processing and machine learning algorithms which also include its various applications. Now we are living in an era where the problem regarding agriculture is a major issue nowadays. The major problem in crop growth is we have to take care of the health of the plants and crops. In this report we basically focused on detection of plants leaf diseases using Image Processing and Machine Learning.

Existing System:

        CNN is used for feature extraction and classification. Though CNN based model producing accurate results, it is very long and time consuming process.

Problem Definition:

          The authors used the CNN model for plant disease detection this is a very long and time-consuming process. Therefore, it is very important to perform advanced machine learning techniques to model and bring out the best accuracy in Machine Learning.

Proposed System:

          Among the machine learning techniques, K-Nearest Neighbors is used as supervised learning models. The KNN mean accuracy increases and ends with higher accuracy. One of the most significant advantages of using the KNN algorithm is that there’s no need to build a model or tune several parameters.

Advantage:

           The KNN algorithm for applications that require high accuracy but that do not require a human-readable model. The quality of the predictions depends on the distance measure.