Efficient management of agricultural resources de-mands a profound comprehension of plant growth dynamics, focusing on sustainable methodologies. This study delves into the forestry sector in Sweden, explicitly addressing the critical early phases of pine tree development within controlled en-vironments, predominantly nurseries and specialised facilities. To tackle the complexities of temporal classification in pine tree growth, we propose a novel measurement system that uses image data to leverage the power of different deep transfer learning-based convolutional neural networks. To classify pine trees over time, our approach integrates different state-of-the-art transfer learning models based on the features extracted by SqueezeNet, MobileNetV2, GoogLeNet, ShuffieNet, and ResNetXt into our measurement system. The primary challenge in tem-porally sorting plant growth is that instances within the same class exhibit variations, whereas instances belonging to different classes may share similarities. We also addressed a pivotal question: whether focusing on the region of interest (ROI) as the input improves the final results. To explore this, we defined two distinct scenarios and conducted a comparative analysis to elucidate the impact of varied input images on the efficacy of our measurement systems. Our experimental results highlight the superior performance of deep SqueezeNet transfer learning over other models, with an error reduction of 15% compared to the second-best approach. Additionally, we provide a sensitivity analysis of the hyperparameters.