A Comparison of Deep Learning Models for Predicting Calcium Deficiency Stage in Tomato Fruits

Identifying and predicting nutritional deficiencies during the growing process of the tomato plant (Solanum Lycopersicum L.) is crucial since mineral nutrients are essential to plant growth. This paper aims to predict and recognize the nutrient deficiency occurring in tomato plants’ flowering and fruiting stages by using two deep learning models, Yolov7 and ResNet50. The study focuses on predicting and classifying tomato plants’ malnutrition stage with an essential mineral nutrient, calcium (Ca2+). ResNet50 and Yolov7 are used to classify three stages of calcium deficiency in tomato fruits by analyzing the captured images of the development of tomato plants under greenhouse conditions. The dataset includes a total of 189 captured images that cover the different levels of calcium deficiency in tomato fruits. Of these, 80% (153 captured images) were used for the training dataset, and 20% (36 captured images) were applied to validate the test dataset. The purpose of this study is to recognize the stage of nutritional deficiencies in order to increase crop yields and prevent nutrient deficiency-related tomato diseases. By analyzing the tomato fruit images captured during tomato plant growth, the performance of ResNet50 and Yolov7 was validated, with accuracy rates of 97.2% and 85,8%, respectively.