title: Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.
abstract: We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.
authors: College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.
authors: Ziyi
authors: Z
authors: Liu
authors: College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.
authors: Junfeng
authors: J
authors: Gao
authors: College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.
authors: Guoguo
authors: G
authors: Yang
authors: Department of Plant Protection, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.
authors: Huan
authors: H
authors: Zhang
authors: College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.
authors: Yong
authors: Y
authors: He
mesh_terms: D000465:Algorithms; D000818:Animals; D002681:China; D018556:Crops, Agricultural / Q000254:growth & development / Q000469:parasitology*; D016208:Databases, Factual; D007313:Insecta / Q000033:anatomy & histology / Q000145:classification* / Q000472:pathogenicity; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated