As a result of animal assaults crop destruction has become one of the foremost threats in decreasing the crop yield. Crop in farms is often damaged via local animals like buffalos, cows, goats, wild animals like bears, monkeys, wild pigs, elephants, etc. and many other birds like sparrows, crows, pigeons. These may cause serious damage to crops which in turn ends in large losses for the farmers. It is difficult for the field owners to build physical barriers to entire field and monitor it. The existing systems is mainly based on observation. Farmers take actions according to the animal that entered their filed. The other ways farmers use to prevent the crop destruction by virtue of animals include constructing barricades, electrical fences, and manual surveillance. Farmers also erect human puppets in middle of fields to ward off animals). When electrical fences are used, the electric shocks produce can be harmful to humans and animals that lead to deaths. AI (Artificial Intelligence) based Scarecrow protects the crops from wild animals with the help of scanning using camera, it detects the stray animals or birds. When it detects the stray animals or birds then it produces a sound of animal extermination. A program must be made with the help of live video for detecting object using YOLOv3 (You Only Look Once), coco names, cv2 modules. It ensures complete safety of crops from animals causing damage to it.

Artificial Intelligence based Scarecrow Prevention from Crop Destruction

This research proposes a ground breaking method for examining the condition of sewage pipes. The underground sewage piping system in cities is a vital form of common infrastructure because it helps to ensure a safe atmosphere. One of the most commonly used sewer inspection process which uses CCTV systems, has a weak performance. A camera is installed on one side of the pipe or on some other unit, and video is recorded within the pipes and sent off-line to an engineer to classify possible faults. The machine-controlled detection and testing of the location of divergences within the internal structure is the subject of this project with the help of Open Source computer vision Library techniques. Many steps are used in the machine-controlled inspection technique, including normalize RGB, Background Subtraction, Canny edge detection, Arc Detect, contours High-light, time Convert, and circular Mask, which involves segmenting the image into Mathematical choices and the defected unit field. Recognizing and classifying defects found within the pipe of an area unit using computation perception techniques helped reconcile image processing. The method of detection is both fast and automatic.

Sewage Pipeline Fault Detection using Image Processing

Plant disease detection using machine learning

Recently COVID-19 has become the most discussed topic in different social media platforms like Twitter, Facebook, Instagram etc. As time moves on, lot of messages and videos are posted in social media. As expected, most of the public followed these messages and becomes panic because of lack of information, misinformation about COVID-19 and its impact. This research study proposes a Twitter sentiment analysisbased on the most popular vaccines Covaxin, Covishield, and Pfizer. Most of the people expressed their feelings about vaccines in the twitter. Twitter API authentication is used here to extract the tweets. These extracted tweets are difficult to analyze, hence pre-processing has been done i.e., unstructured data is converted into structured format. After completion of preprocessing, the data is further classified by using Naïve Bayes algorithm. This algorithm performs data classification and divides it into three major classes as positive, negative, and neutral. The result shows that the covaxin yields 48.36% positive, 35.6% negative, and 16.04% neutral, Covishield yields 44.25% positive, 39.67% negative, and 16.08% neutral, Pfizer yields 42.95% positive, 39.45% negative, and 17.6% neutral sentiment.

I. Haritha

Papers

Sewage Pipeline Fault Detection using Image Processing

Plant disease detection using machine learning

Classification of Covid-19 Vaccines tweets using Naïve Bayes Classification

An approach towards sentiment analysis in social media using machine learning

Malicious activity detection using HTTP annotation patterns