Determination of the effectiveness of artificial intelligence models in detecting maize leaf diseases

Maize is a strategic agricultural product that is of great importance as a source of food and feed; however, leaf diseases cause significant losses in production. The time-consuming nature and high error rate of traditional diagnostic methods have accelerated the search for innovative solutions in agriculture. In this study, the automatic diagnosis of Maize leaf diseases using Convolutional Neural Networks (CNN) was investigated. The dataset obtained from Kaggle, containing images of diseased and healthy leaves, was divided into 80% training, 10% validation, and 10% test sets to compare the performance of different CNN models. The ConvNeXt model demonstrated the highest performance with a 96% accuracy rate, followed by DenseNet with 95% and EfficientNet with 94% accuracy rates. MobileNet stood out with a 92% accuracy rate and low computational cost. The results show that modern CNN architectures provide higher accuracy and efficiency compared to older models. The use of deep learning technologies in agricultural applications holds significant potential in the agricultural sector by offering effective and reliable solutions for disease diagnosis.