BorB: A Novel Image Segmentation Technique for Improving Plant Disease Classification With Deep Learning Models

Disease detection from leaf images has been among the popular studies in recent years. Classifying leaf diseases using computational methods provides great convenience for farming. In the studies carried out in this field, systems that work with high accuracy and are least affected by environmental factors that can be used in agricultural lands come to the fore. This study investigates the application of deep learning architectures for accurate and efficient plant disease detection within the context of the ongoing digital transformation of the agricultural sector. Recognizing the critical role of AI in modernizing agriculture, this research focuses on enhancing the accuracy of the classification of plant diseases. To facilitate this research, a novel dataset, “EruCauliflowerDB”, was meticulously curated, comprising high-resolution images of cauliflower plants infected with Alternaria Leaf Spot and Black Rot. The obtained EruCauliflower dataset contains 114 images from the Alternaria Leaf Spot disease class and 99 images from the Black Rot disease class. A novel integrated classification system was developed, encompassing three key stages. First, a novel segmentation method, “BorB,” was introduced to effectively isolate diseased leaf regions. This segmentation method enables us to extract features of leaf images in Lab and RGB formats. Combining the features obtained from the two image formats with the OR logical operation separates the leaf region from the background. Second, data augmentation techniques, including geometric transformations, were applied to the segmented images to enhance data diversity and improve model robustness. Finally, four state-of-the-art deep learning models—VGG16, ResNet50, EfficientNetB3, and MobileNetV3 Large—were employed for disease classification. The proposed integrated system demonstrated exceptional performance, achieving 100% classification accuracy on the EruCauliflowerDB dataset across all four models. To assess the system’s robustness, further evaluations were conducted on the independent MangoLeafBD dataset, yielding consistent results with 100% classification accuracy. The proposed Integrated Classifier method was applied by selecting 15 classes from the PlantVillage, another multi-class dataset. As a result of the experiments, PlantVillage plant leaf images were classified with 99.78% accuracy. Experimental results show that the proposed method can be effectively utilized in real-world agricultural settings to assist farmers in early disease detection, thereby reducing crop losses and improving yield quality.