Intelligent Tomato Leaf Nutrient Deficiency Detection and Classification Using Convolutional Neural Networks
Keywords:
Convolutional Neural Network, Deep Learning, Tomato Leaf, Nutrient Deficiency, Precision Agriculture, Image Classification, Transfer Learning, Plant Health MonitoringAbstract
Nutrient deficiency in tomato (Solanum lycopersicum) crops is a major factor limiting yield and fruit quality across the world, and its early diagnosis remains largely dependent on manual inspection by agronomists, a process that is slow, subjective, and difficult to scale to large farms. This paper presents an intelligent, image-based system for the automatic detection and classification of nutrient deficiencies in tomato leaves using Convolutional Neural Networks (CNNs). A curated dataset of tomato leaf images spanning healthy leaves and leaves affected by nitrogen, phosphorus, potassium, magnesium, calcium, and iron deficiencies was preprocessed using resizing, normalization, background segmentation, and augmentation techniques to improve model generalization. A custom CNN architecture is proposed and benchmarked against transfer-learning-based models, namely VGG16, ResNet50, MobileNetV2, InceptionV3, and EfficientNetB0. Experimental results show that the fine-tuned EfficientNetB0 model achieves the highest classification accuracy of 97.4%, outperforming the custom CNN (93.8%) and other baseline models, while MobileNetV2 offers the best accuracy-to-computation trade-off for deployment on resource-constrained edge devices. The proposed system demonstrates strong potential for integration into smartphone-based or drone-assisted precision agriculture tools, enabling farmers to obtain rapid, low-cost, and reliable diagnosis of nutrient stress and take corrective fertilization measures in a timely manner.
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