In Asia, there are many countries where paddy serves as the primary agricultural product. Diseases on paddy plants have the potential to reduce paddy production and damage its quality. Detecting the disease earlier will help to prevent serious infection of the paddy and also minimize crop loss. Drones have recently been utilized for agricultural surveillance, along with cameras and GPS sensors. It is a different instrument for swiftly and autonomously obtaining information in a large area. This study describes a drone implementation system that depends upon the Internet of Things (IoT) architecture that uses information in actual instances, information collection, and examination of information strategies to increase paddy productivity. By using a GPS sensor to specify the location in the actual instances, the system is capable of mapping the location of diseased paddy plants on paddy fields along with data analysis. With the installation of the IoT architecture, this framework was created and also put forth as an initial system to support the systems earlier and to detect disease in actual time. 

Prediction of Plant Leaf Diseases using Drone and Image Processing Techniques

This research work presents a novel face recognition system with customized face dataset. After completion of the dataset, this dataset will be fed to neural network (or) model and find how much efficient it is in detecting the student. There are different types of neural networks like ANN and its variants like CNN and RNN. For example, a person is affected by fever or cold etc. In that case, through biometric application disease the can be spread from one person to the other persons. To combat this situation, image processing authentication technique is proposed. This research study develops a new html script for accessing the camera to capture images. The input image is then fed to neural network. Here, a customized CNN model is developed. After recognition, the embedding or features of the image will be compared with the dataset. The proposed model will act like a black box, where input must be provided and it will process the image data and provide the recognized output. 

Smart Face Recognition Application using Deep Learning Neural Networks with Compressed Model Deployment

There are inefficient factors involved in agriculture like diseases in plants, plant nourishment product like inorganic fertilizers, insects and characteristics of soil in which the farmers cultivate crops. One of the profitable agriculture factors is to make proper treatment for plants by spraying organic fertilizers and planning to control the disease occurring in plants. The manual work of spraying fertilizers highly affects the farmers’ health and is time-consuming. Many farmers use drones to help them in their agricultural fields. Both fertilizers and pesticides can be sprinkled in the field with drone technology. Moreover, IoT sensors are being used to achieve a high performance of the technology. In our system, we have proposed the Even Height Maintaining (EHM) Algorithm to maintain the constant gap between plants and drones while spraying pesticides and fertilizing crops. This improves the speed of the fertilization process in agriculture and reduces the cost of drone agriculture technology. Moreover, the use of Artificial Intelligence and Deep learning in the disease detection of crops has been discussed.

Improving Drone Technology Performance In Crop Fertilization

The study examines the use of drone technology in agriculture to predict the occurrence of pest and disease outbreaks. The purpose of the study is to comprehend competition analyses and determine the best entry tactics for the aerial imaging and advice service market. They can determine the current state of drone technology and create strategies based on the different companies providing drone services to farms by studying how farmers perceive the drone employed in predictive pest and disease analyses. These observations are highly valuable for comprehending how drone technology can affect Indian agriculture. A virtual survey gives a useful and accurate image; whether or not they utilise it for predictive forecasting and identification, and if they depend on the advice given by various sources or not, and most importantly, how best the drone service available in the market, whether farmers can afford for it. 

Application of Drone Technology in Agriculture

Hyper-heuristic multi-objective online optimization for cyber security in big data

Drone technology is an emerging technology that is being used for many commercial and survey purposes. This research work has designed a drone technology to develop leaf disease detection and analysis system for agricultural applications. In general, UAV is also known as 'DRONE'. A drone is a device operated without a human pilot and can be remotely controlled with the help of a computer and radio frequency transmitter-receiver system. Identification of plant diseases plays a crucial role in preventing the loss of the yield and quantity of agriculture. A visually observable pattern on leaves helps to study the diseases in plants. Agricultural drones are highly efficient and their usage has been expanded to many areas in agriculture including fertilizer spraying, sowing seeds, growth monitoring, and mapping. This system includes the process of capturing images of the leaves automatically by using a drone with a raspberry pi-camera interface, so that it captures the images for image processing and analysis of leaf properties which would help to detect disease. The suitable algorithms of machine learning (ML) are used after performing feature extraction in the image processing techniques. The edge detection and histogram equalization techniques are used to extract features from the leaf. Drones can also be used in various agricultural applications to reduce human efforts and work time. With the help of GSM, information regarding leaf disease can also be given to the farmer at an early stage and avoid loss in crop production.

Drone Technology Enabled Leaf Disease Detection and Analysis system for Agriculture Applications

This research work has developed an artificial intelligence (AI) enabled smart city IoT system using Edge Computing. To take the smart decision and data processing purpose, this research work has deployed AI algorithms. The novelty in this work is, incorporation of Edge computing in IoT system, which enhances the performance of the IoT system by reducing the load on the cloud. Wi-Fi protocol is used in the network level for data transmission. Raspberry–pi is used to design edge server. The main motivation of this project is to prevent from unexcepted pollution levels in air, water, etc., that causes harmful to the health and also to the nature. So, in this smart city application includes City air management, managing the traffic and transportation, utilization of power effectively, water pollution monitoring. And this monitoring is done by different sensors like camera, gas sensor, water quality sensors, other monitoring sensors. We gather physiological data from the sensors environment and stores it in the database and analyze it to take smart decision and indicates it on the webpage and intimate that information to the through e-mail via SMTP.

Srikhakolanu Naga Venkat

Papers

Drone Technology Enabled Leaf Disease Detection and Analysis system for Agriculture Applications

Artificial Intelligence Enabled Smart City IoT System using Edge Computing