DEVISING A METHOD FOR RECOGNIZING THE CAUSES OF DEVIATIONS IN THE DEVELOPMENT OF THE PLANT ALOE ARBORESCENS L. USING MACHINE LEARNING CAPABILITIES

Abstract

This paper considers the process of developing a method to recognize the causes of plant growth deviations from normal using the advancements in artificial intelligence. The medicinal plant Aloe arborescens L. was chosen as the object of this research given that this plant had been for decades one of the best-selling new products in the world. Aloe arborescens L. is famous for its medicinal properties used in medicine, cosmetology, and even the food industry. Diagnosing the abnormalities in the plant development in a timely and accurate manner plays an important role in preventing the loss of crop production yields. The current study has built a method for recognizing the causes of abnormalities in the development of Aloe arborescens L. caused by a lack of watering or lighting, based on the use of transfer training of the VGG-16 convolutional neural network (United Kingdom). A given architecture is aimed at recognizing objects in images, which is the main reason for using it to achieve the goal set. The analysis of the quality metrics of the proposed image classification process by specified classes has revealed high recognition reliability (for a normally developing plant, 91%; for a plant without proper watering, 89%; and for a plant without proper lighting, 83%). The analysis of the validity of test sample recognition has demonstrated a similar validity of the plant’s classification to one of three classes: 92.6%; 87.5%; and 85.5%, respectively. The results reported here make it possible to supplement the automated systems that control the mode parameters of hydroponic installations by the world’s major producers with the main feedback on the deviation of the plant’s development from the specified values