Deep Learning Techniques for Plant Disease Detection

Deep Learning Techniques for Plant Disease Detection

Asst Prof. Thanish kumar,B. Priya, B. Harshavardhan, B. Karthik Reddy, G. Charan Reddy

CSE AIML, Malla Reddy University, Hyderabad

Abstract: In the lives of humans, agriculture is incredibly vital. Almost 60% of the population is either directly or indirectly involved in agriculture. Farmers are not interested in expanding their agricultural production day by day because there are no technologies in the old system to identify diseases in different crops in an agricultural setting. Early diagnosis of crop diseases is essential since they have an effect on the growth of their particular species. Numerous Machine Learning (ML) models have been used to identify and categorise agricultural illnesses, but new developments in a subset of ML called Deep Learning (DL) seem to hold great promise for increased accuracy in this field of study. The suggested method effectively and precisely recognises the symptoms of crop disease using a convolutional neural network and a deep neural network. Furthermore, these solutions are evaluated using a variety of efficiency indicators. The DL models that are used to depict crop diseases are fully described in this article. Additionally, a number of research holes are found that can provide more clarity for identifying plant diseases even before symptoms appear. Convolution neural network-based technology will be developed using the suggested methodology to identify plant leaf disease.

Keywords: Plant Disease Detection, Deep Learning, Convolution Neural Network, OpenCV