Detection of Diseases in Plants using Convolutional Neural Networks

Most of the global population depends on agriculture and consider agricultural activities as their primary source of occupation to earn their income. If any problem occurs in this primary sector, then it is going to affect the livelihood and lives of the population seriously. Henceforth, it is important to keep up balance in the agricultural area by preventing it from something similar like the adverse effect of plant diseases. The area of artificial intelligence has taken an interesting turn in present times, with the growth of the Neural Networks based Intelligence and Machine Learning. These organically roused computational models can far outshines the presentation of past types of human-made consciousness in like manner artificial intelligence errands. One of the most amazing forms of Artificial Neural Network engineering is CNN. CNN is basically utilized to tackle troublesome picture-driven pattern recognition tasks and with their exact yet straightforward construction, provide a untangle method for starting with ANNs.A new strategy for identification of diseases in plants using CNN is proposed in this paper. The dataset utilized contains around 70,000 images including training and testing dataset. This paper gives a short prologue to CNNs, discussing lately expressed documents and newly framed strategies in evolving these brilliantly tremendous picture recognition models.