http://dspace.dtu.ac.in:8080/jspui/handle/123456789/23 2026-07-01T01:53:58Z 2026-07-01T01:53:58Z SAINI, SHREYA YADAV, TANVI Gupta, Anjana (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22898 2026-06-25T04:53:29Z 2026-05-01T00:00:00Z

Title: PREDICTING CROP YIELDS USING MACHINE LEARNINGWITH SHAP-BASEDEXPLAINABILITY Authors: SAINI, SHREYA; YADAV, TANVI; Gupta, Anjana (SUPERVISOR) Abstract: The agriculture sector is one of the pillars of food security and development world wide. Accurate estimates of crop production are valuable for making decisions in the field of resource planning, supply chain management and policy making. However, con ventional methods of yield prediction, relying on past patterns or basic statistical models, come with limitations in being able to account for the nonlinear relationships found in agriculture. Predicting yield based on environmental, meteorological and agronomic data is a machine learning approach in this project. This data is taken from the Food and Agri culture Organization (FAO) and is available on Kaggle, which consists of 28,242 agricul tural records from 130 countries from 1990– 2013. Five different model regression were developed and used for testing: Linear Regression, Lasso Regression, Ridge regression, K-Nearest Neighbours (KNN) and Decision Tree Regressor. The input variables with temporal (year), climatic (average rainfall, temperature), agronomic (pesticide use) and categorical variables (crop type and geographic region). Handling missing values, one hot encoding of categorical features, and feature standardization using a scikit-learn’s ColumnTransformer pipeline comprised the preprocessing steps. Results have shown that DT Reg performed best on generalization with an R² value of 0.9793 and Mean Squared Error (MSE) of 3941, an improvement of 23% over the baseline LR model (R² = 0.7473). More interpretability was achieved by using the SHapley Additive exPlanations (SHAP) analysis: crop type, especially potatoes, turned out to be the most important and dom inating factor, while pesticide use and climatic factors followed. These nonlinear rela tionships were identified using feature-level analysis via SHAP dependence plots, with pesticide use showing a strong negative correlation with yield for high productivity sce narios, and temperature and rainfall having complex interactions, which varied by crop type and region. The project is finalized with the deployment-ready predictive system, and the discussion of practical applications in precision agriculture.

2026-05-01T00:00:00Z VANITA Kumar, Dhirendra (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22897 2026-06-25T04:53:22Z 2026-05-01T00:00:00Z

Title: AN ENHANCED DIVERGENCE-BASED DISTANCE MEASURE FOR INTUITIONISTIC FUZZY SETS WITH HESITATION INFORMATIONAND ITS EXTENSIONS TO INTERVAL-VALUEDAND PICTURE FUZZYENVIRONMENTS Authors: VANITA; Kumar, Dhirendra (SUPERVISOR) Abstract: Distance measures for intuitionistic fuzzy sets (IFSs) are central tools for pattern recognition, clustering, and multi-attribute decision making (MADM) under uncertainty. Although numerous divergence-based and geometric distance mea sures have been proposed in the literature, most of them either neglect the hesita tion degree, which carries genuine epistemic information, or fail to discriminate between sets that exhibit equal membership-non-membership differences but distinct hesitation profiles. To overcome these limitations, this paper proposes a novel enhanced divergence-based distance measure D+ L for IFSs that incor porates an explicit hesitation term derived from a modified Kullback–Leibler divergence. The measure is constructed from a single, symmetric core function and is shown to satisfy all four axiomatic requirements of an IFS distance metric, namely boundedness, separability, symmetry, and monotonicity. The proposed measure is further extended to two important generalizations of intuitionistic fuzzy theory: a six-term version DIV+ L for interval-valued intuitionistic fuzzy sets (IVIFSs) that fully exploits both the lower and upper bounds of member ship, non-membership and hesitation intervals, and a three-component version DP L for picture fuzzy sets (PFSs) that handles positive, neutral, and negative memberships. Six classical benchmark cases and two innovation-management decision problems are recomputed entirely from scratch using the proposed measure. Comparative analysis with twelve existing measures shows that D+ L resolves the counter-intuitive ties that plague competing measures, distinguishes hesitation-sensitive cases that earlier divergence-based measures could not, and yields stable rankings in TOPSIS-based MADM. The results confirm that the enhanced measure is mathematically rigorous, computationally concise, and practically effective.

2026-05-01T00:00:00Z RAJAN GUPTA, TRASHA (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22896 2026-06-25T04:53:11Z 2026-05-01T00:00:00Z

Title: LAND USE AND LAND COVER CLASSIFICATIONUSING SATELLITE IMAGERY AND DEEP LEARNING Authors: RAJAN; GUPTA, TRASHA (SUPERVISOR) Abstract: The precise and effective monitoring of land use and land cover (LULC) is important for urban planning, environmental protection, agriculture, and disaster management. Current approaches for classifying satellite images are dependent on the manual inspection process, which is time-consuming and can cause errors. Hence, a deep learning and computer vision-based solution for efficient LULC classification has been presented in this dissertation. For the purpose of developing our model, we used the EuroSat data set. It is a publicly available data set generated from the images collected by the Sentinel-2 satellite, which is owned and operated by the European Space Agency. Our deep convolutional neural network (CNN), inspired by the LeNet-5 architecture, is intended to classify satellite images of size 64x64 into ten classes such as Annual crop, Forest, Highway, Residential area, and Water bodies. For implementation, we used PyTorch, the popular deep learning framework, in conjunction with a GPU and CUDA. As a part of this dissertation, we have also considered the use of transfer learning. For that, we froze all but the last layer of a ResNet-18 CNN trained on the Imagenet database. It was shown through experiments that the proposed lightweight custom CNN model was able to achieve a validation accuracy of 66.9% in just five training iterations while taking only 2.44 minutes to train on the GPU altogether. The findings show that although deep learning models present themselves as a more reliable option compared to manual classification techniques, the similarity of spectra among vegetation classes cannot be ignored as the source of any possible mistakes. The findings suggest that small CNN architectures are an excellent choice for achieving real-time environmental monitoring in a more effective manner, and transfer learning with ResNet-18 represents a way forward to obtaining better quality. In total, these two concepts present a great platform for making further improvements using the multispectral analysis method and pixel-wise semantic segmentation techniques. (Keywords: Land Use and Land Cover (LULC), Convolutional Neural Network (CNN), EuroSAT, Transfer Learning, ResNet-18, PyTorch, Remote Sensing, Sentinel-2, Deep Learning, Image Classification.)

2026-05-01T00:00:00Z KHATRI, VINITA Singh, Chandra Prakash (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22894 2026-06-25T04:52:54Z 2025-12-01T00:00:00Z

Title: A STUDY ON EVOLUTION AND DYNAMICS OF DARK ENERGY MODELS IN COSMOLOGY Authors: KHATRI, VINITA; Singh, Chandra Prakash (SUPERVISOR) Abstract: Cosmology is a branch of science which deals with the study of the origin of Universe, its evolution, and its eventual fate. Cosmology has been classified into two categories in contemporary science: Physical cosmology and Observational Cosmology. The study of the Universe’s formation, evolution, and the physics behind it, is known as physical cosmology. Observational cosmology investigates the direct evidence of the Universe’s formation and structure using telescopes and other tools. Cosmological models are studied using the combination of theories and observations. These models incorporate ideas as well as data gathered from observations. Cosmology integrates developments from a wide range of scientific fields, such as relativity, quantum mechanics, particle physics, nuclear physics, astrophysics, and plasma physics. Nicolaus Copernicus’ discovery that the Earth rotates around the Sun in the early 1500s marked the beginning of modern cosmology. Isaac Newton’s discovery in the late 1600s that objects in space functioned in accordance with the same rules of physics as objects on Earth was an additional breakthrough. Early in the 20th century, Albert Einstein’s theory of relativity provided a model of spacetime, establishing the way to modern physical cosmology. Contemporary cosmologists assert that the Universe is made up of far more than the ordinary matter we encounter daily. Most scientists believe that a significant portion of the Universe is composed of Dark energy and Dark matter. According to this theory, about two-thirds of the Universe is composed of dark energy. The dark energy is believed to be the force that defies gravity and permits the Universe to expand- a phenomenon known as cosmic acceleration. According to this concept, dark matter makes up an additional 26% of the cosmos. Scientists are unable to directly detect this hypothetical kind of matter since it does not emit or absorb light and only interacts with regular matter through gravity. The most plausible and effective candidate of dark energy is the cosmological vii constant introduced by Albert Einstein. Several alternative models have been proposed to explain the Universe’s observed accelerated expansion which includes scalar field theories, Chaplygin gas, holographic dark energy and Ricci dark energy, and several other dark energy models. The decaying vacuum energy density and bulk viscosity have recently been investigated as additional potential explanations for the universe’s current accelerated expansion. The objective of this thesis is to investigate how decaying vacuum and bulk viscosity contribute to the explanation of dark energy phenomena in the context of a spatially homogenous and isotropic flat Friedmann-Lemaître-Robertson-Walker metric in general relativity and its modified theories. Using observational data, we fit the model to the proposed model and extract the relevant information on the decaying vacuum energy density and bulk viscosity. The first chapter serves as an introduction. The research that was published as research articles in reputable, peer-reviewed publications served as the basis for chapters 2–6. The conclusion and future directions of the thesis work are covered in the final chapter. Each chapter starts with an overview of the work accomplished in that chapter.

2025-12-01T00:00:00Z