Improving prediction of plant disease using k-efficient clustering and classification algorithms

Abstract

<span lang="EN-US">Because plant disease is main cause of most plants’ damage, improving prediction plans for early detection of plant where it has disease or not is an essential interest of decision makers in the agricultural sector for providing proper plant care at appropriate time. Clustering and classification algorithms have proven effective in early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of care quality provided to similar plants. Thus, in this article, we present an artificial intelligence (AI) model based on k-nearest neighbors (k-NN) classifier and k-efficient clustering that integrates k-means with k-medoids to take advantage of both k-means and k-medoids to improve plant disease prediction strategies. Objectives of this article are to determine performance of k-mean, k-medoids and k-efficient also we compare k-NN before clustering and with clustering in prediction of soybean disease for selecting best one for plant disease forecasting. These objectives enable us to analysis data of plant that help to understand nature of plant. Results indicate that k-NN with k-efficient is more efficient than other in terms of inter-class, intra-class, normal mutual information (NMI), accuracy, precision, recall, F-measure, and running time.</span>

Retracted: Smart optimal path with blockchain modelling in internet of things using enhanced K‐means clustering and advance encryption standard

“…The K-Means algorithm starts with each participant in their own cluster and then gradually merges them together to form a larger cluster [22]. The distance is typically used to compare and contrast the characteristics of different data objects.…”

Classification and Prediction of Crop Diseases: A Review

“…K-NN with k-efficient surpasses other methods in inter-class, intra-class, normal mutual information, accuracy, precision, recall, F-measure, and running time. [18] H. Tabassum et al examine how plant leaf diseases affect agriculture, given that many Indians depend on farming. ML algorithms for plant leaf disease detection include SVM, Naive-Bayes, LR, KNN, ANN, CNN, Back Propagation, Genetic algorithm, and PNN.…”