Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification

Abstract

Abstract In recent years, many countries prolonged their research in the field of agricultural farming for yielding more quantity food products with good quality. Agricultural farming is closely related to every country's economy in significant way. To improve the grain productivity in a notable way, plants must be protected from various viral, fungal, and bacterial diseases. The timely identification of plant disease helps to protect the plant at the early stage of the disease else it will spoil the entire plant. In this article, the plant disease identification problem is handled using our novel proposed network where it combines dilated convolution with residual dense block (DCRDB) along with multi‐level feature detection (MLFD) for selecting the appropriate feature and bidirectional long short‐term memory (Bi‐LSTM) classifier for leaf disease prediction. In DCRDB, the dilated convolution derives larger receptive field without loss of resolution to extract greater number of local features from the leaf images and residual dense block expands the size of feature map by densely connecting the layers of convolution. In addition, MLFD network integrates the high‐level features with the resulted features of DCRDB. After feature extraction, Bi‐LSTM is employed to recognize the leaf disease which supports additional training in both forward and backward direction. Through a set of collected cassava leaf images our research carries forward. The proposed model accuracy is verified with three different datasets such as cassava disease classification dataset, self‐made cassava disease dataset and plant village dataset. The performance of the proposed model is compared with some existing methods. Experimental results show that the highest F1‐score value of 95.49% attained by our proposed model for identifying cassava leaf disease.

Plant phenotyping relevance

カッサバ葉画像から病害状態を推定する深層学習ネットワークを提案し、複数データセットで性能検証しており、植物フェノタイピング手法が中心である。

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