Abstract
The convolutional neural networks (CNNs) approach for image classification has a scope to identify the different plant leaf disease. It is possible to implement a innovative way of training the algorithm and methodology enable a good performing and easy system design for practical application. The developed model can recognize three types of leaf diseases and pesticides and/or fertilizers are advised according to the severity of the diseases. The type of green leaf diseases is recognized by CNN. After recognition, the predictive remedies are suggested that can help agriculture related people and organizations to take appropriate actions against these diseases. The image analysis is done by using k-means clustering algorithm which help to group and differentiate the plant leaves based on disease.
Abstract from the paper’s OpenAlex record; open copy licensed other-oa. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 4
Claim · model-written summary
The developed model can recognize three types of plant leaf diseases.
Verbatim
e0“The developed model can recognize three types of leaf diseases”
e1“plant leaves”
Claim · model-written summary
The system advises pesticides or fertilizers according to disease severity.
Verbatim
e0“pesticides and/or fertilizers are advised according to the severity of the diseases”
e1“plant leaves”
Claim · model-written summary
A CNN recognizes the type of green leaf disease.
Verbatim
e0“The type of green leaf diseases is recognized by CNN.”
e1“plant leaf disease”
Claim · model-written summary
A k-means image analysis groups and differentiates diseased plant leaves.
Verbatim
e0“The image analysis is done by using k-means clustering algorithm which help to group and differentiate the plant leaves based on disease.”