Predicting Phenological Stages with Deep Neural Network Models to Increase Efficiency in Tomato Production

Nowadays, tomatoes are one of the most widely cultivated crops globally and domestically. However, insufficient nourishment during the phenological stages of tomato seedlings can negatively impact their productivity. This study examines the accuracy of VGG19, ResNet101, and MobileNetV2 models in predicting the phenological stages of tomato plants. The SVM algorithm is used to classify the features obtained using these architectures, and the performance of the resulting models is evaluated. MobileNetV2+SVM has shown significantly superior performance compared to other models, with an accuracy rate of 98.75%. The MobileNetV2+SVM’s lightweight structure and computational efficiency demonstrate potential for high-accuracy classification even in resource-constrained environments. The characteristics of this model make it well-suited for use in agricultural and robotic applications. The high accuracy rates enable the precise application of the nutrient solution to each tomato seedling’s phenological stage, boosting agricultural productivity.