Temporal Image Sequence Fusion with PSO-Optimised CNN Transfer Learning for Plant Temporal State Categorisation

The accurate classification of plant growth stages from image sequences is a challenging problem due to the gradual and continuous nature of plant development. Significant variability exists within the same category of growth, and temporal boundaries between successive categories often exhibit visual similarities, complicating the classification task. Whereas the existing approaches are limited and rely on the analysis of single frames, they do not take temporal information embedded in sequential data, reducing robustness and accuracy. They also show vulnerability to intra-category variability among the images and also to inter-class overlap at the temporal boundaries. To address these challenges, we propose a novel approach, PSO-SqueezeTempVote, for plant temporal state categorisation that combines PSO for hyperparameter optimisation of the SqueezeNet architecture with Temporal Image Sequence Fusion (TISF) for temporal ensemble learning. The PSO part effectively optimises some critical hyperparameters, hence improving the performance of the model on individual frames. Meanwhile, the component of TISF exploits temporal continuity using a sequence of images’ aggregation through majority voting. This enables classifying the segment of an image much more precisely than the previously done approaches. Results on our datasets confirm that the PSOSqueezeTempVot has improved the state-of-the-art techniques by almost 13%, out of which approximately 6% was contributed by TISF itself.