Plant Disease Detection and Diagnosis using Deep Learning

Growth of a country is dependent on agriculture. Food is the basic nutrition for human health. However, we observed that plant being the origin of nourishment is drastically affected by the ailments. Plant Diseases are the alarming issue which needs to be addressed. In the era where technology is fully flourished still some farmers cannot avail the expert's advice due to financial restrictions and additional constraints to travel long distances to consult. Plant Malady Recognition for farmers is a time-consuming task and requires the experts visit on field frequently to monitor the status. An accurate and early detection of plant diseases will assist farmers to diagnose effectively and hence the economic losses in agriculture can also be reduced. Deep Learning models is more beneficial as it requires no human intervention while performing image processing, feature extraction and has a significant advantage over other strategies like Machine Learning algorithms which can solve well-structured problems. But for more complex data applications we require Deeper Architectures and hence our methodology consists of using Convolutional Neural Network (CNN) to identify and diagnose the plant diseases likeBlackmeasles, Scab, Earlyblight, Leafscorch and Bacterialspot.