Visual Classification of Lettuce Growth Stage based on Morphological Attributes using Unsupervised Machine Learning models

Food shortage is a serious problem facing the world and is prevalent in urban areas. The scarcity of food is mainly caused by crop failure. Environmental factors offered by the rural areas determine the condition of crops to be produced. This scenario pomps, the explication of urban farming. However, urban farming requires all-out monitoring and control. This study specifically solves the predicament of identifying the developmental growth of plants from seed leaf to amend the techniques of plant science and cultivation management. With a view to this, the paper shows coupled color-based superpixels and multifold watershed transformation in segmenting the lettuce image from the background. To fathom it out, a comparative analysis of three unsupervised machine learning algorithms: Self Organizing Map (SOM), Hierarchical, and K - means algorithms were conducted. These were done by modeling each algorithm from the features extracted from morphological computations of the lettuce images raised in a smart aquaponics setup. Each of the models was optimized to increase cross and hold-out validations. The results showed that K – means algorithm having the parameters of algorithm = ‘auto’, copyx= ‘True’, init = ‘K- means++’, maxiter = ‘1000’, nclusters = ‘3’, ninit = ‘15’, n_jobs = ‘1’, precompute_distance = ‘auto’, random_state = ‘10’, tol = ‘0.000001’, verbose = ‘1’, leaf_size = ‘10’ was the most effective model for the given dataset, yielding a high precision and recall unsupervised clustering percentage of 91%.