A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models

A Novel Hybrid Severity Prediction Model for Blast Paddy Disease Using Machine Learning

Hypothesis: Due to the increase in the losses in paddy yield as a result of various paddy diseases, researchers are working tirelessly for a technological solution to assist farmers in making decisions about disease severity and potential danger to the crop. Early prediction of infection severity would facilitate resources for the treatment of the infection and prevent contamination to the whole field. Methodology: In this study, a hybrid prediction model was developed to predict various levels of severity of blast disease based on diseased plant images. The proposed model is a four-fold severity prediction model. The level of severity is defined based on the percentage of leaf area affected by the disease. The image dataset is derived from both primary and secondary resources. Tools: The features are first extracted with the help of the Convolutional Neural Network (CNN) approach. Then the identification and classification of the severity level of blast disease are conducted using a Support Vector Machine (SVM). Conclusion: Mendeley, Kaggle, GitHub, and UCI are the secondary resources used for dataset generation. The number of images in the dataset is 1908. The proposed hybrid model achieves 97% accuracy

Distally based peroneus brevis muscle flap: A single centre experience

Purpose: Defects around the distal one third of the leg and ankle are difficult to manage by conservative measures or simple split thickness skin graft. Distally based peroneus brevis muscle flap is a well described flap for such defects. Methods: This is a retrospective analysis conducted on 25 patients with soft tissue and bony defects of distal third of lower leg and ankle, which were treated using distally based peroneus brevis muscle flap from January 2013 to January 2018. Information regarding patient demographics, etiology, size and location of defects and complications were collected. All patients were followed up for at least 3 months after surgery. Results: There were 21 males and 4 females with the mean age of 39 (5–76) years. The most common cause of injuries was road traffic accident, followed by complicated open injury. The average size of defects was 20 (4–50) cm2. The mean operating time was 75 (60–90) min for flap harvest and inset. We had no patient with complete loss of the flap. Five patients (20%) had marginal necrosis of the flap and two patients have graft loss due to underlying hematoma and required secondary split thickness skin grafting. Conclusion: The distally based peroneus brevis muscle flap is a safe option with reliable anatomy for small to moderate sized defects following low velocity injury around the ankle. The commonest complication encountered is skin graft loss which can be reduced by primary delayed grafting. Keywords: Peroneus brevis flap, Reverse sural artery, Tendoachilles, Lower leg defect