EXPLORATORY DATA ANALYSIS FOR LEAF DISEASE DETECTION - Revathy M; Elangovan, Karthikeyan (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Agriculture plays a significant part in India due to their population growth and increased food demands. Hence, there is a need to enhance the yield of crop. One of these important effects on low crop yields is diseases caused by bacteria, fungi and viruses. This can be prevented and handled by means of applying plant disease detection approaches. Machine learning techniques will be employed in the process of disease identification on plants as it mostly applies information themselves and offers fabulous techniques for detection of plant diseases. Analysis of the sickness should be done appropriately and proper movements must be taken at the correct time. A correct detection of leaf disorder is crucial for plant culture as well as the rural financial system. Even though many works were executed for identifying leaf disease, due to the inadequate strategies additionally the obligations about classifying leaf disorder is difficult to be expecting This paper explores the Ada Boost with Naïve Bayes perform well as well it showing an efficient outcome. It has the greatest accuracy result of 85.75%. The Ada Boost with Naïve Bayes produces the greatest precision result of 0.86. The Ada Boost with Naïve Bayes and Stochastic Gradient Boost with Naïve Bayes produce the maximum recall of 0.86. The Ada Boost with Naïve Bayes has the greatest F-Measure result of 0.86. The Ada Boost with Naïve Bayes model has the highest MCC value of 0.65. The Ada Boost with Naïve Bayes model has the greatest kappa value of 0.66. The Ada Boost with Naïve Bayes model has an optimal results compare with other models.