Insect detection and classification based on improved convolutional
To identify agricultural pests by a CNN system ...
Regarding the growth of crops, one of the
important factors affecting crop yield is insect disaster.
Since most insect species are extremely similar, insect
detection on field crops, such as rice, soybean and other
crops, is more challenging than generic object detection.
Presently, distinguishing insects in crop fields mainly
relies on manual classification, but it is extremely
time-consuming and expensive. This work proposes a
convolutional neural network model to solve the
multi-classification of crop insects. The model can make
full use of the advantages of the neural network to
comprehensively extract multifaceted insect features. During
the regional proposal stage, the Region Proposal Network is
adopted rather than a traditional selective search technique
to generate a smaller number of proposal windows, which is
especially important for improving prediction accuracy and
accelerating computations. Experimental results show that
the proposed method achieves a heightened accuracy and is
superior to the state-of-the-art traditional insect
Sample images of 24 insect species collected
from crop fields.
Source codes: supplementary.
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