Kuldeep Singh Kaswan

Bio: Kuldeep Singh Kaswan is an academic researcher from Galgotias University. The author has contributed to research in topics: Blockchain & Smart grid. The author has an hindex of 1, co-authored 8 publications receiving 3 citations.

Detection of Corn Gray Leaf Spot Severity Levels using Deep Learning Approach

Abstract: A simple Convolutional neural network (CNN) based deep learning (DL) model has been proposed for multi-classification of corn gray leaf spot (CGLS) disease based on five different severity levels of CGLS disease on the corn plant. Certain corn leaf diseases like CGLS, common rust, and leaf blight are quite common and dangerous in corn harvest. Hence, the current work presents a solution for CGLS disease detection on corn plants using a multi-classification DL model which gives the best detection accuracy of 95.33% in high-risk severity level image. Along with this comparison of five different severity levels has also been conducted based on resulted performance measures (PM).

Smart Grid Using Internet of Things

Abstract: The IoT (internet of things) is a network of people and stuff at any moment, anytime, for anyone, with any network or service. IoT is therefore a major complex worldwide network backbone for online service providers. The smart grid (SG) is one of IoT's main applications. SG is an interconnected data exchange network that gathers and analyzes data obtained from transmission lines, generation stations, and customers through the power grid. The internet of things has risen as the basis of creativity for energy grids. The chapter is based on the idea that, if one grid station transmitting electricity to customers is cut off due to some defects of IoT-based systems, all grid station loads can be connected to another system so that power is not disrupted. The authors discuss the IoT and SG and their relationship in this chapter. The best advantages for SG and specifications can be addressed in the SG works, creative innovations using IoT in SG, IoT software, and facilities in SG.

Blockchain Assembled Supply Chains to Foster Secure Trading Using Distributed Ledger

Abstract: In this paper, we study how to assemble a supply chain with the avail of Blockchain to establish a secure trading environment. Blockchain is distributed, decentralized and a transparent mechanism which makes it suitable to share data on publicly accessible networks such that it can further be verified and audited independently thus maintaining data integrity. This paper suggests that the Blockchain technology offers great potential to foster secure trading using distributed ledger that propounds ingenious platforms as it makes the history of any digital asset unalterable, traceable and transparent. We further study how Blockchain mitigates the subsisting quandary in supply chains, how does it provide an edge over other technologies. The model is proposed to implement the Blockchain manufactured supply chain and it is further explained how it eliminates the challenges faced by the current supply chain management system. Also, challenges to adopting this technology have been discussed in the latter section of the paper.

GAN based image augmentation for increased CNN performance in Paddy leaf disease classification

Abstract: A variety of fungal and bacterial leaf ailments wreak havoc on the paddy plant in the agricultural field. Early diagnosis of leaf infection can improve the yield of the crop. The modeling of an automatic disease classifier aids farmers in handling the spread of leaf disease in the agricultural field. This work cogitates three paddy leaf diseases (Bacterial blight, leaf smut, and leaf blast) for the creation of an AI-based robust detection and classification model. The dataset is collected from a variety of standard online repositories. GAN-based augmentation technique was used for increasing the size of the dataset. A novel approach to the convolutional neural network is proposed with the combination of augmentation and a CNN model tuner. The performance of CNN is evaluated in terms of accuracy achieved is 98.23\% in the classification process.

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

Abstract: 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.