Plant Diseases Concept in Smart Agriculture Using Deep Learning

1. Introduction

Agriculture performs a vital role within the global economy. Pressurize to the agriculture system (Coakley et al. 1999) will enhance with the continuing growth of the human population. Agriculture is the backbone of the Indian economy. The enormous substantial commercialization of the agricultural system impacts our climate quite severely (Solomon et al., 2007). The use of chemicals has contributed to tremendous amounts of chemical building up in water, food, soil, animals and also in our inner bodies. Synthetic fertilizers have a small-term effect on production, however a longer-term detrimental impact on ecosystem. Another adverse impact of this development has been at the circumstances of the worldwide agricultural communities (Cerri et al., 2007).Despite the matter of expanded productivity, farmers have undergone a decline agricultural fortunes in almost every country across the world. It is here that organic farming begins. Organic farming has the potential to deal with every of those problems and its main practice depends on fertilization, control of pests and diseases. Especially in throughout agricultural sector, where crop types are influenced by output productive patterns and management of resources, disinfestation levels (Mahlein et al. 2013), irrigation, productivity attention and efficient improvements (Ngugi et al., 2020) are coveted; maintaining these production rhythms without the use of any automatic controlling is likely to lead in resource loss, rotting and wasted crops polluted with impoverished soils. Agri-technology and precision farming, also called digital farming, have emerged as modern scientific disciplines that use data-intensive approaches of maximizing agricultural productivity while reducing its environmental effects. Data produced in modern farming procedures are provided by a number of different sensors (Reddy et al. 2015) that allow a clearer understanding of the operating environment (an interface of complex crop, soil, and climate conditions) and the process itself (machine data), resulting in more precise and quick decision making.

The challenge of effective protection of plant diseases is similarly linked to sustainable agriculture and environmental change issue factors. Detection of plant disease (Jadhav et al., 2020) by examining the effects on plant leaves with a naked eye, integrates increasingly growing constraints (Karthik et al., 2020). Deep learning one of the new, modern technique that used for processing images and analyzing featured data with precise outcomes and great potentiality. Considering that deep learning implemented significantly in related disciplines and recently penetrated to agricultural realm (Boulent et al., 2019).