巴基斯坦

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.

Over 30 million souls live in a world of darkness, a number greater than the populations of both Norway and Sweden combined. Every individual deserves the chance to embark on a journey across our magnificent blue planet. Yet, regrettably, little has been done to assist them. With this project, we’re lighting the way for the blind to explore our beautiful world independently, breaking free from dependence and embracing boundless horizons. In order to put our theory of the project into practice & explore the use of artificial intelligence & computer science, we started by collecting the required materials for our project such as micro-controllers, sensors, a pair of glasses, a laptop, and a miniature camera. Then we moved onto creating the project itself in which the digital software programmed onto the hardware plays the key-role, as the sensors and the camera will record the details and information from the surroundings and send it to the laptop for further processing. The camera would be the backbone of our project, as it will stream real-time footage to the laptop which will be analyzed by an open-source object detection model ‘YOLOv8’ for identifying objects. After finishing the base model of our project, we tested it in-front of objects such as toy cars, bikes, people, etc, and the results of the object-detection would be shown on the laptop. To observe this data, we created a device which has different modules and integrations for different functions. For example, we will use our camera and then stream it onto a laptop so the reading and the data can be processed on the laptop by AI using YOLOv8. As mentioned in the start, many people do not possess the ability to see, to assist them we have thought of this device which uses all readings and its analytical skills to analyze data and help them navigate, travel or simply, live a better life.

As the world population grows, the demand of food products grows as well and there will be an expected food crisis in the coming years. To prevent those crises, alternative food farming methods must be used. This paper studied two farming systems in different conditions, to compare and find the best, natural and cost-effective system that will cover the current and future demand. The system which can also be used in those areas where soil is less cultivated with insufficient aeration. The first system is the soil-based system (traditional), and the other is hydroponic system. Hydroponic is a technique of growing plants in nutrient solutions with or without the use of an inert medium. Two types of seeds; peas and spinach were observed in both systems over a period of 25 days. In hydroponic plants coco peat was used in place of soil along with the Aegis nutrient. 8 plants were seeded for both types of plants in different systems, conditions, concentrations and pH to conclude the best condition. Growth parameters of all plants including root, shoot and leaf length were observed and recorded daily. On the uprooting, their weight (g), no. of root hairs and used nutrient’s volume(ml) were also recorded. Fungus and insects were seen in the soil plants. The results executed that the growth was maximum in spinach having normal manufacturer nutrient’s spray concentration(1.25ml/625ml) with pH 6 and in peas having normal supplier concentration (5ml/625ml) with pH 4. So, it can be concluded that hydroponic spinach, which is a green leafy plant, can ideally grow at the pH of 6 and peas in slightly acidic condition. Hydroponic planting system has a better growth effect than traditional soil system and this system don’t need any fertilizer, insecticide, pesticide, fungicide and herbicide. While soil plants’ growth was adversely affected by fungus and insects in the absence of these chemicals which can contaminate our food and make it less hygienic for our health. This result achieves the aim of this paper which is finding a planting system and its conditions that can increase the productivity to cover the food demand.

As the global demand for sustainable agriculture practices and renewable energy sources continues to rise, the integration of solar power technology with irrigation systems has gained significant attention. This abstract presents an overview of an innovative solution known as the "Automatic Solar Panel Sprinkler Irrigation System," which combines solar panels and smart irrigation technology to efficiently manage water resources in agricultural settings. The proposed system leverages solar panels to generate electricity and simultaneously operate an automated sprinkler irrigation system. Solar panels are strategically positioned in proximity to crop fields, utilizing photovoltaic cells to convert solar energy into electrical power. This energy is then harnessed to power the irrigation system, providing a sustainable and eco-friendly method for crop hydration. In Solar Power Generation the system consists of photovoltaic panels designed to capture solar energy during daylight. This renewable energy source is converted into electrical power, which is stored in batteries for subsequent use. Automated Sprinkler Irrigation is an advanced control system manages the irrigation process, ensuring efficient water distribution based on crop requirements. Soil moisture sensors and weather data are integrated to optimize irrigation scheduling. In Remote Monitoring and Control, farmers can remotely monitor and control the irrigation system through a user friendly interface, accessible via smartphones or computers. This feature enables real-time adjustments and ensures that water resources are utilized optimally. In Water Conservation the system is designed to minimize water wastage by delivering precise and targeted irrigation, reducing over-watering and the associated environmental impact. In Cost Savings the harnessing solar power, this system reduces electricity expenses, making it an economically viable solution for farmers, particularly in regions with ample sunlight. Using a tracker with an automatic solar panel sprinkler irrigation system can be a smart and efficient way to optimize the system's performance. And the most important thing is that in my prototype it is under the panel and will track the location of the Sun and with that it will lead to the Automatic movement of the panel from east to west and when the evening starts it will go back to its position.