Plant Disease Detection using CNN Model and Image Processing
Md. Tariqul Islam
Abstract
The rate of plants and crops cultivation rates growing rapidly with the increment of human and animal demands all over the world. The agricultural science invented lots of authentic techniques to use in cultivation sector for improve the production rate. During cultivation, farmers faced lots of challenges to protect their plant form different diseases and insects which make their production lower and they faced much financial losses. Form decades, agricultural scientists tried hardly to invent a quick medication system for detect plant disease quickly and gives treatment immediately. But the identification techniques of fetal abnormalities of plant are manual and it takes huge time. For reducing detection time and increasing efficiency of plant disease detection lots of new technologies introduced with cultivation system. In this work we introduced a model with the help of computer science and engineering using machine learning specially deep learning for detecting the leaf disease by the image of "Corn", "Peach", "Grape", "Potato" and "Strawberry". In Bangladesh, Mize and Potato is very popular food item and Strawberry is also very appealing for all aged people. Peach and Grape is not so popular cultivation in our country but their attraction in fruit market skyrocketed. The farmer who is interested and involved to cultivate corn, peach, grape, potato and strawberry faces lots of disorder and attract of insects and huge loss faced by the farmer. For prevent these losses and provide immediate cure, in this paper uses image processing, CNN (Convolutional Neural Network) model for training the dataset. Our system provides accuracy rate 94.29% successfully. The universal cultivator can get assistance from our research work for growing the production rate of crops and fruits and alleviate the plant disease and insect attract.