The document describes a method for identifying plant leaf diseases using a Raspberry Pi, K-nearest neighbors algorithm, and deep neural networks. Images of plant leaves are processed using convolutional neural networks, which use multiple focal points to identify diseases. This method can accurately identify leaf diseases at a 90% rate when provided a good dataset. Being able to easily identify plant diseases will help increase agricultural productivity while reducing chemical use on crops.

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Raspberry pi based leaf disease detection using KNN and deep neural network.
Abstract: -
For identifyingdiseasesforplantswe use nakedeye.But it is not accurate so SVMis usedfor prediction
whichislessaccurate 70 % evenif datasetisgood.so,thispaperwe use Raspberrypi for identificationof
the diseases.Here we use the convolutional neural networksforprocessingof images.CNN usesvarious
focal pointstoidentifythe diseasesinthe plants.Inresearchpurposesitisveryusefulbecausebythiswe
can identifydiseasesof leafcorrectlyusingtheCNN algorithm.Diseaserefersto typeof the planteffected.
Here we use the best algorithm to identify and predict the plant lead disease. Easily identification of
diseaseswillbe veryhelpful because itwill increase the productivityandreduce chemicalsonthe plants.
It will increase agriculture yield with minimumcost which is very helpful now a days and we can get
accuracy of 90% if dataset is good.