A Transfer Learning CNN approach for automated Plant Growth Temporal Labelling: Addressing Class-Based Variability Paradox and introducing novel metrics

Recognising the growth and transformation of plants over time is of great significance for both agriculture and environmental preservation. It aids stakeholders in efficiently utilising resources and providing optimal growth conditions. This paper investigates the automated Plant Growth Temporal Labelling (PGTempLab) problem, which involves plant growth classification based on visual imagery—specifically focusing on pine seedlings, a key forest species in Scandinavia. A central challenge of PGTempLab lies in the Class-Based Variability (CBV) Paradox, where significant intra-class variability and inter-class similarity occur due to temporal categorisation, complicating traditional classification tasks. To address this issue, a transfer learning approach is adopted using 12 well-known convolutional neural networks (CNNs) as pre-trained models. Additionally, three novel evaluation criteria are introduced, incorporating temporal aspects: time-dependent accuracy (TDA), temporal boundary deviation (TBD), and a temporal confusion matrix. Experiments, conducted on a custom dataset collected specifically for this study, consisting of over 38,000 time-lapse images of pine seedlings captured from two camera perspectives over 44 days, demonstrate that MobileNet achieves the highest performance in the top-view setting with 86.85% TDA, while SqueezeNetV2 performs best in the angle-view setting with 86.94% TDA. These results highlight the effectiveness of deep transfer learning in addressing the CBV paradox in plant growth classification.