Plant leaf disease detection using ensemble classification and feature extraction

Abstract

It is a well-known certitude that agriculture and cultivation has a momentous part to play in the world in the economy of the world including the countries where agriculture is the sole source of income. Nevertheless, it is quite unfortunate that this is being affected by destruction caused by diseases. Plants form a considerable origin for energy for both man and animal. Leaves of the plants are the primary way the plants interconnect with the earth’s atmosphere. Hence, it becomes prime responsibility of the researchers and academicians to look into the matter, develop schemes to pinpoint the leaves infected with diseases. This will help the farmers around the world to take timely steps to retain their crops from damaging to an ultimate extent and save the world and themselves from the possible economic crisis. Detecting the diseases laboriously may not be a right idea and hence a mechanized way to identify leaf diseases which can be a boon for the agricultural sphere which will also bloat the crop yield. This paper focuses on the use of ensemble classification along with hybrid features of Law’s mask, LBP, GLCM, SIFT and Gabor in order to improve the results of classification. The proposed approach shows that ensemble of the chosen classifiers can perform much better as compared to individual classifiers. Since ensemble classification showed better accuracy, the choice of the features also matter to get the best possible results. The experiments have been performed on the diseased leaf images of bell pepper, potato and tomato of the PlantVillage dataset