Identification of Leaf Diseases of Different Crops using modified ResNet50

Abstract

Every country’s income depends heavily on the agriculture sector. Agriculture output is influenced by a wide range of factors, including crop health, weather, water availability, and type of land. Low output is primarily caused by crop disease. The Identification and treatment of diseases early on are very crucial. Traditional approaches don't offer a reliable evaluation. The spread of the disease must be determined using automated techniques. One of the cutting-edge technologies that aids in automating disease detection is deep learning. Deep learning has been used in numerous studies to detect crop diseases. The goal of this research is to effectively diagnose leaf diseases in all major crops using a novel deep learning model using a modified ResNet50. The research employed different deep learning models like VGG16, Inception V3 and modified ResNet50 on publicly available dataset of plant village. A novel approach has been proposed by extracting features from different models. The ResNet50 uses the skip connection which is considered to training and test error. The modified ResNet50 model helps add diversity in different crops for providing accuracy across all crops on public dataset of plant village. The dataset was obtained from a publicly available database on Kaggle. The dataset contains 13 different crops and a total of 38 diseases classes. On modified plant villages the proposed model generated a very good accuracy of 99.49% with precision of 0.966 and f score of 0.995. The proposed model is reliable for the classification of various leaf diseases in all the major crops.