Analyzing the Model Performance using Machine Learning Algorithms for Plant Disease Detection

  • D.Lakshmi Padmaja, M.BalaSuresh, G. K.Sriharsha, G.N.V Ramana Rao, N.Nagalakshmi, T.Asha Latha


This paper focus on the detection on plant diseases and pesticide suggestion for the plant diseases. Plants are essential for human life as they provide food and oxygen which are important to live. As the human beings effected by the diseases the plants are also affectby the various diseases and if the proper care is not taken the infected plant/crop can’t give proper yield which indirect leads in food crisis.So, it is important to identify the infected crop and the specific pesticide is used to control the disease. Using machine learning experiments on various fields proves the good results. In plant diseases, identification and pesticide detection the machine learning and deep learning algorithms can be used and by which the disease is identified and the pesticide can be suggested. The disease canidentify by using various CNN architecturesand pesticide is suggested by using various machine learning classification algorithms. The precision, recalland F1 score are considered to analyze the model performance.

Keywords:Plant Diseases; Pesticides; CNN architectures; Machine Learning Algorithms