Novel encoding technique to evolve convolutional neural network as a multi-criteria problem for plant image segmentation

Abstract

Despite the success of deep convolutional neural networks (DCNNs) in various applications, optimizing them for specific tasks remains challenging due to the complex manual tuning of hyperparameters. This approach is often ineffective when balancing multiple objectives, as it relies heavily on trial and error. This study proposes an innovative multi-objective genetic algorithm (MOGA) approach to automatically learn highly efficient and resource-saving DCNN architectures, in short, termed as MOGA-DCNN, for a given plant image segmentation task. To this end, a novel encoding technique was proposed to simplify the structure of the candidate solutions and constrain the search space in such a way that a Pareto set of non-dominated solutions can be explored efficiently through genetic operators, reducing computational complexity, and improving performance. We have evaluated this approach on different datasets collected from fruit trees and oilseed rape crops under controlled and uncontrolled conditions. The results demonstrated the capability of the proposed MOGA-DCNN to automatically construct variable-length DCNN architectures for each dataset. The storage size of the architecture parameters (71 K) was only 0.24 % of the well-known SegNet. The evolved model classifies an image 15 – 18 times faster than DeepLab v3+, indicating an overwhelming advantage in image segmentation. These results suggest the prospect of model transferability to different image segmentation tasks, and it could be integrated into embedded system devices with an extremely low computational cost.

Plant phenotyping relevance

植物画像セグメンテーションのためのCNNアーキテクチャを遺伝的アルゴリズムで開発・評価しており、植物表現型取得の中核手法である。

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