Revolutionizing Potato Agriculture: Harnessing Machine Learning Techniques for Disease Detection and Management
Aim: The aim of this study is to make a disease-predicting model trained on data from weather stations and API using machine learning that gives the farmer the ability to predict crop diseases before they set in, allowing them to take timely preventative measures and reduce wastage. Materials and Methods: In this study the Internet of Things (IoT) sensors throughout agricultural fields of potato crops in Jafferabad, Depalpur Punjab. The sensors collect real-time data on environmental conditions, such as precipitation, air temperature, relative humidity, wind speed, and direction, Dew Point, VPD, and the Delta T values, to identify subtle disease indicators and patterns within the environmental data. Our novel machine-learning program makes use of the data collected by the weather station and analyses them. Results: Using the data, one predictive statistical method using Python 3.8.0 was created which uses the data from the weather station which can predict diseases before they set in.
TEST & SAVE
Electricity has become an essential part of modern life, powering homes, businesses, and industries. However, the misuse of electricity or malfunctioning electrical systems can lead to hazardous situations such as electrical fires, shocks, and significant energy wastage. This project focuses on creating a Comprehensive Electrical Security System to protect users and properties from the risks associated with electricity. The system is designed to prevent electrical malfunctions, ensure safety in various scenarios, and monitor energy consumption effectively. It integrates a variety of sensors and safety mechanisms to detect dangers and take preemptive action
MEDTEC - Artificial Intelligence Software for medical diagnosis optimization and analysis
In Brazil, approximately sixty million people suffer from or acquire some type of disease daily. However, the average time for blood count diagnoses, used to identify many of these diseases, remains very lengthy. This can lead to the worsening of conditions and delays in care, as well as a decrease in the patients’ quality of life. Moreover, in some cases, the waiting period can result in irreversible situations and even the death of the affected individuals. In this landscape, technological tools such as artificial intelligence software can help reduce the time taken for diagnostic reporting. In light of this, the project involves developing software to assist in the analysis of blood counts and optimize medical diagnoses. For this purpose, the methodology was divided into three stages. In the first, titled ”Medical Standardization”, a survey of the standard variables related to diseases that can be identified with the help of blood counts was conducted. Among the findings, diabetes, anemia, leukemia, dengue, polycythemia, tuberculosis, leprosy, meningitis, chlamydia, schistosomiasis, spotted fever, and malaria were the main diseases detected. Furthermore, hemoglobin, leukocytes, platelets, glucose, cholesterol, ions, and hormones were the key findings concerning the primary blood indicative factors for the mentioned diseases. In the second phase, the theoretical and practical foundations of the software were developed, based on artificial neural networks. In Python, regression models were also crafted to check the feasibility of the analyses. Finally, the last stage consisted of testing with real datasets, based on 1,227 anonymized blood counts. Among the artificial intelligence algorithm models tested, Support Vector (0.02) and Multiple Linear (0.61) had the lowest performances, while Polynomial (0.97), Random Forest (1.0), and Decision Tree (1.0) showed the best results. Given that the Random Forest and Decision Tree regression models achieved an accuracy of 1.0, while the Polynomial model scored 0.97, Support Vector 0.02, and Multiple Linear Regression 0.61, it is concluded that the blood count analysis system, with Python tools like regression, proved to be highly efficient. The closer the R² value is to 1.0, the better the programming fits the model, ensuring accurate analyses. Aside from that, in order to expand the number of analysis possible to do be done we decided to use a second tool called ”classification”, with which we made a bigger dataset to be used as a model to identify blood related diseases and the behavior of complex and diverse diseases. With that in mind, we performed a second evaluation of the models by doing an accuracy test, scored 87 percentage points and with a confusion matrix. With those results, we verified that the high performance of the tests indicates that Artificial Intelligence can be avaunt-guard to the elaboration of more efficient medical diagnosis, improving people’s lives quality and, overall, lowering the number of deaths in our country.
THIRD-LIFE: Real Life Accident Alerting, Live Locations and Notifications to Emergency Service
The country of Nepal, although beautiful, is facing many challenges due to its geography, lying between the towering Himalayas and the vast plains of Terai. The narrow mountain roads, prone to landslides and poor infrastructure, often result in frequent accidents. This situation is worsened by the delayed emergency response, as accidents are often reported much later than the time they occur. In the past ten years, over 15 major bus accidents have killed hundreds of people, and in 2024 alone, more than 80 deaths were reported. In response, the "Third Life" project was developed to improve emergency response time and save lives.The project has two main components: first, a device equipped with GSM (Global System for Mobile Communications), a GPS module (Global Positioning System), a gyroscopic sensor, and a microcontroller to detect accidents in real-time within seconds of the incident. Second, once an accident is detected, live coordinates are sent directly to emergency services and police stations for immediate assistance.This project is not only vital for Nepal but also for countries with similar terrain and infrastructure challenges. The "Third Life" project aims to save many lives that are lost due to delayed reporting, ensuring quicker emergency responses.A tragic example of this was the 2024 Trishuli bus accident, where many lives were lost when the bus plunged into the river. To date, the bus has not been recovered. Our project aims to create a waterproof device that, when connected to a satellite, will send live coordinates to emergency services, ensuring 100% reliability. This device could help locate the bus, which is still missing, within seconds.Ultimately, this initiative offers more than just safety it restores peace of mind and hope for the families of victims, providing them with a chance for a better future despite the tragedy.