International Scientific Journal of Engineering and Management

An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database & Metadata
ISSN: 2583-6129

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SMART PEST DETECTION

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SMART PEST DETECTION

H Sudhakar[1]                   M Sindhu[2]                            N Gowtham[3]                    M Haripriya[4]                                     M Bhagavan[5]                      M Sasidhar[6]

Asst. Professor, Sir C R Reddy College Of Engineering[1] UG Scholars, Sir C R Reddy College Of Engineering[2,3,4,5,6]

ABSTRACT: An automatic approach for early pest detection. Agriculture not only provides food for the human existence, it is also a big source for the economy of any country. Millions of dollars are being spent to safeguard the crops annually. Insects and pests damage the crops and, thus, are very dangerous for the overall growth of the crop. One method to protect the crop is early pest detection so that the crop can be protected from pest attack. The best way to know about the health of the crop is the timely examination of the crop. If pests are detected, appropriate measures can be taken to protect the crop from a big production loss at the end. Early detection would be helpful for minimizing the usage of the pesticides and would provide guidance for the selection of the pesticides. It has become a wide area for research now a days and a lot of research has been carried out worldwide for automatic detection of pests. Traditional method of examination of the fields is naked eye examination but it is very difficult to have a detailed examination in large fields. To examine the whole field, many human experts are needed which is very expensive and time consuming. Hence, an automatic system is required which can not only examine the crops to detect pest infestation but also can classify the type of pests on crops by using the neural networks which we had already given in the data sets .finally we attained pest detection with an accuracy of 99%..

KEYWORDS : Matlab, Convolutional neural network (CNN), pests, Classification, Object detection, Segmentation.


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