Popular GitHub Repositories for Crop

Image augmentation for machine learning experiments.

A python tool that uses GPT-4, FFmpeg, and OpenCV to automatically analyze videos, extract the most interesting sections, and crop them for an improved viewing experience.

AI-Video-Cropper is a Python-based tool that leverages the power of GPT-4 (OpenAI's language model) to automatically analyze videos, extract the most interesting sections, and crop them for improved viewing experience. This project combines the capabilities of GPT-4, FFmpeg, and OpenCV to automate the process of identifying highlights in videos

A machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.

Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

A natively parallel dataloader for Python, written in Rust. Serving data at GB/s speeds, while covering aspect ratio bucketing, crop and resize for image ML workloads.

AI-based voice-assisted Contact Center for assisting Farmers for their problems.

[RSE 2021] Crop mapping from image time series: deep learning with multi-scale label hierarchies

A platform of machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.

A React, Flask and Machine Learning based web application for Crop and Fertilizer Recommendation

Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

PixelPruner is a user-friendly image cropping app for AI-generated art. It supports PNG, JPG, JPEG, and WEBP formats. Easily crop, preview, and manage images with interactive previews, thumbnail views, rotation tools, and customizable output folders. Streamline your workflow and achieve perfect crops every time with PixelPruner.

Developed a machine learning-based crop prediction model to assist farmers in making informed decisions about crop selection, planting, and harvesting.Integrated weather and geolocation APIs along with a web page for simplified user experience.

The proposed system will be able to predict the crop yield production which will be useful to farmers for harvesting and storage. The system will use the weather forecasting which includes the parameters like temperature, rainfall, humidity, dew point and the normalized difference vegetation index time series from Sentinel-2 satellite for selected region. By using both the results obtained,accurate crop yield prediction can be calculated which will help farmers in planning efficiently, minimize costs and maximize yields—and profits—as a result.

[TGRS21] Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations

Agro Doc is basically an app that will help farmers easily pinpoint their crop diseases using their smartphones. The app uses a pre trained tensorflow model to identify issues and then suggest possible cures for the crop infections/diseases. #AndroidDevChallenge

Smart Farming Assistant uses advanced technology, including machine learning and CNNs, to provide farmers with crop recommendations, disease identification, weather forecasts, fertilizer recommendation, and crop management guidance through a user-friendly web app.

A system that is capable of automatically irrigating the agricultural field by sensing the parameters of soil in real-time and predicting crop based on those parameters using machine learning. The system also predicts the yield of the crop.

EuroCropsML is a ready-to-use benchmark dataset for few-shot crop type classification using Sentinel-2 imagery.

DigiFarmer is an Artificial Intelligence and Machine Learning based project which can perform various operations/functions related to farming prediction such as Crop Quality, Yeild Prediction, Disease Detection and Weed Detection, etc. This Project is build using Flutter with dart and for backend we used the ML model's as TenserflowLite.