Implementing a Compression Technique on the Progressive Contextual Excitation Network for Smart Farming Applications.

Abstract

The utilization of computer vision in smart farming is becoming a trend in constructing an agricultural automation scheme. Deep learning (DL) is famous for the accurate approach to addressing the tasks in computer vision, such as object detection and image classification. The superiority of the deep learning model on the smart farming application, called Progressive Contextual Excitation Network (PCENet), has also been studied in our recent study to classify cocoa bean images. However, the assessment of the computational time on the PCENet model shows that the original model is only 0.101s or 9.9 FPS on the Jetson Nano as the edge platform. Therefore, this research demonstrates the compression technique to accelerate the PCENet model using pruning filters. From our experiment, we can accelerate the current model and achieve 16.7 FPS assessed in the Jetson Nano. Moreover, the accuracy of the compressed model can be maintained at 86.1%, while the original model is 86.8%. In addition, our approach is more accurate than ResNet18 as the state-of-the-art only reaches 82.7%. The assessment using the corn leaf disease dataset indicates that the compressed model can achieve an accuracy of 97.5%, while the accuracy of the original PCENet is 97.7%.

Plant phenotyping relevance

植物画像から病害状態を分類する深層学習モデルについて、枝刈りによる圧縮・高速化と精度検証を主題としており、表現型推定手法の開発・検証が中心である。

Code and data availability

The paper describes a compressed PCENet evaluated on a private cocoa bean image dataset (collected by the authors) and the publicly available corn leaf disease dataset [18], but no author code, trained models, or dataset deposit is mentioned anywhere in the supplied blocks. The corn leaf disease dataset is cited prior/

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