Artificial intelligence assisted tomato plant monitoring system – An experimental approach based on universal multi-branch general-purpose convolutional neural network

[Display omitted] •DeepD381v4plus integrations were discussed to address task-specific challenges.•Although the symptoms of the diseases are similar, they can still be distinguished.•It can classify diseases even with 27 fps). Overall, the proposed experimental approach will help farmers prevent disease outbreaks, monitor flower shapes that can set fruits at the highest rate, timely detect and count ripened fruits or recognise damaged fruits due to surface cracks or diseases for harvesting at their optimal maturity stage. This will reduce the labour costs, improve cultivation management and ensure excellent quality of the harvested tomatoes.