A Guava Leaf Disease Detection by Machine Learning

Abstract

Fruit diagnosis and early identification The production of healthy fruit industry is more critical for plant diseases. Farmers' general monitoring system can take time, costly and often incorrect. This paper offers an overview of target recognition through grouping of numerous images and machine learning methods for the guava leaf disease detection. Our system has been developed based on machine learning algorithm. In this work rust, white fly, leaf spot and sound disease has been detected. For the whole method, a number of machine learning programs (MLs) were used, such as Scikit-learn, Pandas, Matploatlib, Numpy. In the pre-processing of images, we have also used Scikit-learn to implement algorithms. In order to check the validity of our work we use five separate K-Nearest Neighbour(KNN), Vector Support (SVM), Tree Classifier Decisions and the Random Forest. Naive Bayes. The most effective algorithm. This five algorithms were studied. Finally, this high-precision algorithm detects guava leaf disease.