A non-invasive approach for calcium deficiency detection in pears using machine learning

A pear is a sweet fruit that is rich in dietary fiber, antioxidants, and plant compounds. The nutritional disorder in pears is either due to deficiency of nutrients or toxicity of nutrients. The techniques to identify the nutrients deficiencies include tissue testing, soil analysis, plant analysis, and visual deficiency symptoms. The effects of alfalfa greening, black end, and cork spot are minimised by correcting the calcium nutrition in the pear tree. In this paper, a two-class decision jungle model is proposed for the recognition of calcium deficiency in pears based on a non-invasive approach. The calcium deficiency in pears makes a bumpy fruit surface and leaves yellow color on the affected area than the rest of the skin results in the greyish corky lesion. The nutrient deficiency that results in serious disorders in pears not only influences the plant but also impacts the fruit quality. The introduction of artificial intelligence in the agriculture industry has helped farmers to produce healthier fruits. The artificial intelligence provides a real-time data for the classifier that results in increasing agricultural efficiencies, better crop yields and reduce fruit production costs by facilitating the routine and most complex tasks. The two-class decision jungle model achieves an accuracy of 98% with a database of 1000 samples. The other approaches, such as Boosted decision tree, Bayes point machine, Logistic regression, Neural Network, and SVM, have an accuracy of 92.20%, 84.3%, 72.5%, 82.4%, and 72.5%, respectively for the equivalent datasets. The highest accuracy is achieved with the proposed two class decision jungle that has non-linear decision boundaries and the performance is resilient in the presence of features that consist of noise. The number of calcium-deficient and healthy pears is 500 each. The geometrical features are extracted for the development of an artificial intelligence-based model for the classification of two classes like calcium deficient and healthy pear. The extracted features are split into training, validation, and testing. For training, validation, and testing, 80%, 10% and 10% samples are used respectively. The precision level is observed to be 0.974 and test accuracy is achieved as 98.7% and the overall accuracy 98% which are better than the existing 88.2% accuracy for pears using Support Vector Machine.