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

Asraa Safaa Ahmed, Zainab Kadhm Obeas, Batool Abd Alhade, Refed Adnan Jaleel


Because plant disease is the main cause of most plants' damage, improving prediction plans for the early detection of the plant where it has a disease or not is an essential interest of decision-makers in the agricultural sector for providing proper plant care at the appropriate time. Clustering and classification algorithms have proven effective in the early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of the care quality provided to similar plants. Thus in this article, we present an Artificial Intelligence (AI) model based on the 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. The objectives of this article are to determine the performance of k-mean, kmedoids, and k-efficient clustering also compares K-NN classification before clustering and with clustering algorithms in the prediction of soybean disease for selecting the best one for plant disease forecasting. These objectives enable us to analyze data of plants that help to understand the nature of plants. The results indicate that K-NN with K-Efficient clustering is more efficient than other algorithms in terms of inter-class, intra-class, Normal Mutual Information, accuracy, precision, recall, F - Measure, and running time.



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