Application of Siamese neural networks to classify plant biomass by visual state

This paper proposes a method for classifying plant biomass by visual condition using images captured in a specially designed greenhouse and Siamese architecture artificial neural network technologies. Criteria for various states of plant biomass have been determined. We have generated our own dataset for training Siamese neural networks, containing samples of biomass states in the form of textures. As a result, a training accuracy of 91.6% and an average classification accuracy of individual biomass states of 73.6%. (In Russian).