Automated Detection of Plant Diseases- A Promising Tool for Smart Agriculture

Abstract

Smart agriculture utilizes modern technologies to increase crop productivity. The productivity of agricultural crops has to be elevated for food security for the ever-increasing global population. The production of agricultural crops is largely affected due to increasing infestation of diseases and pests in addition to abiotic stresses. Early detection and management of diseases hold key to tackling the challenge. The disease pest infestation can be controlled by applying pesticides and insecticides. But numerous negative health effects that have been associated with chemical pesticides have been well documented. The increasing computational technology and recent advances in deep learning have paved the way for rapid disease diagnosis and management. Here we have discussed the automatic detection and classification of plant diseases as well as their severity through Image Processing. The detection of disease and its degree of severity from images is based on colour, texture and shape and gives a fast and accurate solution through the use of smart computational tools. DNNs (Deep Neural Networks) and CNNs (Convolutional Neural Networks) are effective in the detection, recognition and classification of plant diseases towards an automated solution for the large-scale agricultural industry.