Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

The main objective of this project is to identify overspeed vehicles, using Deep Learning and Machine Learning Algorithms. After acquisition of series of images from the video, trucks are detected using Haar Cascade Classifier. The model for the classifier is trained using lots of positive and negative images to make an XML file. This is followed by tracking down the vehicles and estimating their speeds with the help of their respective locations, ppm (pixels per meter) and fps (frames per second). Now, the cropped images of the identified trucks are sent for License Plate detection. The CCA (Connected Component Analysis) assists in Number Plate detection and Characters Segmentation. The SVC model is trained using characters images (20X20) and to increase the accuracy, 4 cross fold validation (Machine Learning) is also done. This model aids in recognizing the segmented characters. After recognition, the calculated speed of the trucks is fed into an excel sheet along with their license plate numbers. These trucks are also assigned some IDs to generate a systematized database.

Crop-Disease-Detection-via-Image-Processing uses machine learning and image analysis to identify plant diseases from leaf images. The system preprocesses images, extracts features, and applies a trained model to classify diseases accurately.

PEST DETECTION USING IMAGE PROCESSING e The principal idea which empowered us to work on the project PEST DETECTION USING IMAGE PROCESSING is to ensure improved and better farming techniques for farmers. Our Solution: The techniques of image analysis are extensively applied to agricultural science, and it provides maximum protection to crops and also much less use of pesticides which can ultimately lead to better crop management and production. The following softwares are required for the project: OpenCV with C++/Python : It is a library which is designed for computational efficiency with a strong focus on real time applications. Pest Detection System Following are the image processing steps which are used in the proposed system. >Color Image to Gray Image Conversion Therefore, images are converted into gray scale images so that they can be handled easily and require less storage. The following equation shows how images are converted into gray scale images. I(x,y)=0.2989*B +0.5870*G +0.1140*B > Image Filtering The PSNR value is calculated for both the average and median resulting images .The average filter provides better result as compared to the median filter. So this paper uses average filter for further processing. > Image Segmentation To detect the pests from the images, the image background is calculated using morphological operators which is most critical after this image is subtracted from the original image. So the resulting image will only have the objects with pixel values 1 and background pixel values 0. >Noise Removal Noise contains dew drops, dust and other visible parts of leaves. As only the object of interest was to be visible on the images,so the aim was to remove the noise to get better and effective results. The Erosion algorithm has been used to remove isolated noisy pixels and to smoothen object boundaries . After noise removal,the next goal was to enhance the detected pests after segmentation which was performed by using the dilation algorithm. >Feature Extraction Different properties of the images are calculated on the basis of those attributes using which image is classified. For image properties, gray level co-occurrence matrix and regional properties of the images are calculated. These properties are used to train the support vector machine to classify images. >Counting of the pests on the leaves is the main purpose, so that it can give an idea of how much pests are there on a leaf.It uses Moore neighborhood tracing algorithm and Jacob's stopping criterion Feasibility: The present framework of pest detection is quite tedious and laborious for the farmers as they have to carry out their acre-acres surveys themselves and it requires a lot of vigorous efforts to achieve the same.Image analysis provides a realistic opportunity for the automation of insect pest detection.Through this system, crop technicians can easily count the pests from the collected specimens, and right pests’ management can be applied to increase both the quantity and quality of production. Using the automated system, crop technicians can make the monitoring process easier. So in order to bring enhancements in the system,we came up with more productive and well organised system with our idea .Due to this automaton applied,lucrativeness increases and labour is reduced.

UAV (Unmanned Aerial Vehicles) can be made capable of providing a health monitoring system for plants. High end cameras gives deep insights whether it is surveillance or any broadcast of events (filming and production). The key benefits of crop analysis includes identifying for plant diseases and crop health by inspecting its colour components. This is done by studying Near Infrared and Multi-Spectral Cameras which help us in obtaining the Red, Green, Blue and Infrared components of a plant and with the help of computer vision. With computer aided image processing. The processing of the video feed captured by a NoIR camera is done with the help of an on-board micro-computer. Additionally autonomous flight, safety failsafe for landing when on low power, mission planning etc. are some of the facilities that can be incorporated in such a UAV to provide a full integrated crop monitoring and analysis system.

Analysis of satellite images/NDVI images to obtain sowing and harvesting periods of crops in a region of interest.

Designed to assist in identifying crop diseases through image analysis and providing relevant care guides.

Image Crop Comparator (ICC): A research-oriented, interactive image crop comparator for pixel-level method analysis, designed for a fast and flexible interactive workflow with multi-ROI selection, multi-image preview, automatic layout arrangement, and rich customization options.

AI & LoRa-Based Microclimate Advisory for Smart Farming is a frontend prototype of a smart agriculture dashboard. It allows farmers to upload crop images for disease analysis, view simulated microclimate sensor data, and receive advisory suggestions through an interactive and user-friendly interface.

My Submission to the Twitter Algorithmic Bias Challenge.

DEMETER is an AI-powered plant disease detection web app that helps farmers identify crop diseases early through image analysis. Users register, enter basic crop details, and upload leaf images. A trained deep learning model analyzes the photo and predicts disease presence, offering actionable recommendations.

AgriNext is an AI-powered web application designed to help farmers identify crop diseases instantly through image analysis. Built with Python, TensorFlow, and Streamlit, this tool enables users to upload images of plant leaves and receive immediate disease diagnosis along with tailored treatment recommendations.

This repository showcases deep learning projects from the WQU Applied AI Lab, focusing on computer vision applications. Projects include wildlife classification, crop disease detection, traffic flow analysis, face recognition, medical image generation, and a meme generator app. Built with PyTorch and Python.

AI & LoRa-Based Microclimate Advisory for Smart Farming is a frontend prototype of a smart agriculture dashboard. It allows farmers to upload crop images for disease analysis, view simulated microclimate sensor data, and receive advisory suggestions through an interactive and user-friendly interface.

AgriTech Assistant is a smart agricultural platform empowering Indian farmers with crop yield prediction, plant disease detection through image analysis, and a multilingual chatbot to answer farming queries in Indian languages. It offers actionable insights and real-time support for sustainable and productive farming practices.

This program is to crop raw UAV image by plot polygon file by using reverse calculating. It is part of the open source programs designed for the field of plant phenotype analysis. See details of the method in the following papers: Duan, T. et al. Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Functional Plant Biology 44, 169 (2017). Tresch L. et al. Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping. Plant Phenomics 2019, 1–9 (2019). Contributor: Yue Mu (Plant Phenomics Research Center, Nanjing Agricultural University) Wei Guo (International Field Phenomics Research Laboratory, The university of Tokyo) Bangyou Zheng (CSIRO Agriculture and Food)

Basic image analysis tools in python (image cropping, contour property calculations etc.)

Framework to analysis the images with cropped cosmic-ray based noise on CMOS/CCD camera

Semi-automatic (manual cropping) image analysis for gel electrophoresis lane profiling

Tech meets agri in our Plant Disease Detection Project! 🌱🔬 Open-source ML & image analysis combine to safeguard crops, ensuring sustainable yields. Join us to nurture greener tomorrows. #PlantHealth #AIforAgri

Smart Crop Yield Prediction using AI - 8 ML models, crop image scanning, live weather, NDVI analysis, soil health, smart farming recommendations for Indian agriculture

Dealing with the essential of image processing concepts(grayscale, binary, downscalling, analysis(mean, standard deviation, entropy and histograms), enhancement, flipping, blurring, negative, cropping and searching images).

🌿 AI-powered tool for farmers — recommends crops at sowing stage and detects diseases during growth using ML and image analysis.

AI-powered web application for crop disease prediction using image analysis, designed with Lovable AI for a hackathon to support smart farming and early disease detection.**

This project uses a Convolutional Neural Network (CNN) to identify diseases in rice crops based on image analysis. Trained in Jupyter Notebook, the model achieves an accuracy of 89%, helping farmers detect crop issues early for better yield and productivity.

DigiFarmer is a modern, AI-powered web application that revolutionizes agricultural decision-making by combining advanced computer vision and machine learning technologies. Upload a soil image and receive intelligent crop recommendations based on soil classification and environmental analysis.

This repository contains an end-to-end pipeline for UAV-based precision agriculture using multispectral imaging, NDVI analysis, and AI-driven vegetation classification. The system integrates computer vision, machine learning, and geospatial mapping to assess crop health and optimize agricultural practices.

This project analyzes the detection and the growth stages of wheat crop by capturing a digital image of the crop from time to time by a Raspberry pi Camera Module V2. This image is then transferred to a computer for analysis. The analysis is done by the deep learning algorithm Convolutional neural network CNNs

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Goes 16 exploratory data analysis and reproject and crop image.