An Evolutionary Compact Deep Transfer Learning with CNN for Hyper-Parameter Tuning in Temporal Sorting of Plant Growth

The efficient management of agricultural resources requires a deep understanding of plant growth dynamics. This research focuses on Sweden's forestry sector and explicitly addresses the crucial early stages of pine tree development. The main difficulty in categorising plant growth over time is that instances within a given category are not identical, while instances from different categories may have similarities. In this context, we present a novel measurement system that integrates the capa-bilities of evolutionary computation and deep transfer learning using image data. The image acquisition system includes a tray of plates that moves through a nursery, generating a dataset captured over 44 days of plant growth. Our newly proposed algorithm, EvoSqueezeNet, employs various search strategies for SqueezeNet deep transfer learning to find proper hyper-parameters. We opted for SqueezeNet based on our preliminary studies, revealing its superior performance compared to other pre-trained models in our case study. Given that our approach is independent of any specific evolutionary algorithm, we utilised five distinct search strategies. These include Differential Evolution (DE), Particle Swarm Optimisation (PSO), Covariance Ma-trix Adaptation Evolution Strategy (CMA-ES), Comprehensive Learning PSO (CLPSO), and Linear Population Size Reduction Success-History Adaptation DE (LSHADE). Consequently, we proposed five EvoSqueezeNet schemes for temporal plant growth categorisation. One characteristic of our proposed model is that it uses a limited computation budget for search strategies, en-hancing its applicability in real-world applications. The proposed EvoSqueezeNet methodology demonstrates an error reduction of more than 40%, showcasing its superior performance compared to competing methods.