A5101867350- Smart Agriculture and AI
- Spectroscopy and Chemometric Analyses
- Agricultural Science and Fertilization
- Plant Disease Management Techniques
- Crop Yield and Soil Fertility
- Leaf Properties and Growth Measurement
- Rice Cultivation and Yield Improvement
- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- Usability and User Interface Design
- Health and Conflict Studies
- Potato Plant Research
- Web Data Mining and Analysis
- Nuts composition and effects
- Plant Physiology and Cultivation Studies
- Neutrino Physics Research
- Agricultural Economics and Practices
- Tree Root and Stability Studies
- Autism Spectrum Disorder Research
- Technology and Security Systems
- Banana Cultivation and Research
- Pomegranate: compositions and health benefits
- Plant tissue culture and regeneration
- Genetics and Plant Breeding
- Personal Information Management and User Behavior
Institute of Management Technology
2022-2024
Dr. A.P.J. Abdul Kalam Technical University
2024
Galgotias University
2021-2023
University of Chicago
2018
Potatoes are an important crop heavily consumed by Indian food products. It is produced on a massive scale, with China, India, Russia, Poland, and the USA being main producers. Numerous leaf diseases harm during its production. A typical farmer lacks tools necessary to detect Leaf Disease before damage done. On dataset of potato images retrieved from Kaggle, we employed EfficientNetB0 Deep Learning address this problem. This model uses width scaling resolution apart depth perform...
Agriculture is a vital domain for the economic development of many countries. For farmers, it essential to choose an appropriate crop plant depending on variety environmental parameters. In order predict best cultivation considering soil quality, climate, and other criteria, this article provides system recommendations employing machine learning, especially Random Forest algorithm (RF). The methodology involved collecting data from various sources, pre-processing data, selecting relevant...
Agriculture is the main earnings-producing field as well a cause of livelihood in India. Different biological variables and seasonal financial factors affect yield growth, but unexpected variations these result major loss crops. When adequate mathematical or statistical techniques are applied to information related soil, climate, previous yield, hazards can be quantified. With advancement machine learning, crop yields may anticipated by extracting helpful from fields that assist government...
Rice (Oryza sativa) is the globe's favorite eatable grain. It important diet for more than half of population as a source energy. Abiotic and biotic elements such weather, soil fertility, temperature, pests, pathogens, viruses, others influence rice grain yields production amount quality. Farmers invest lot time energy in disease control, they detect diseases with their penniless human eye strategy, which causes unhealthy cultivation. To avoid biased, incorrect, inefficient manual detection,...
The demand for food grains is increasing as the world's population grows. Half of country's relies on paddy a source. Most farmers struggle with challenges in early detection rice leaf disease. Rice diseases such Blast, blight, and tungro can be detected using transfer learning models, allowing us to prevent disease spread over entire plant thus yields. Using techniques DenseNet201, suggested system has been used identify crops. This research focuses three well-known diseases: fungus-caused...
Rice is one of the most widely grown crops across globe, and it susceptible to a variety illnesses at various phases production. Crop pathogens have devastating influence on food safety, as well considerable loss in both quantity quality Plant infections can damage crop entirely severe circumstances. Farmers' poor understanding makes extremely difficult for them visually diagnose these diseases. As result, agriculture domain, automatic recognition diagnosis diseases are greatly needed....
We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate application of domain adversarial (DANNs) this context. DANNs are designed be trained one (simulated data) but tested second (physics utilize unlabeled data from so that during training only features which unable discriminate between domains promoted. is neutrino-nucleus scattering experiment NuMI beamline at...
Machine learning, a subset of Artificial Intelligence, has gained much recognition in facilitating disease prediction and the decision-making process healthcare. One most often diagnosed developmental disorders world is Autism Spectrum Disorder (ASD). Around world, it reported to afflict 75 million people number cases gradually increased since studies began 1960s. The symptoms generally include communication deficits, sensory processing differences, repetitive actions or behaviors. This...
The main objective is this paper to develop an application that "Java-based software robot for automatic email extraction" and helps extract emails from the web quickly as it very tiring e-mails manually. E-mails are primary source transmitting information over internet. useful in giving about any conference formal business meetings. It highly used organizations send regarding events held organization. But time-consuming We can use these ad campaigning promotional messages. This proposed...