Popular GitHub Repositories for Yield Farming

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

A bot for establishing WebSocket connections through various HTTP and SOCKS proxies for farming Grass Airdrop.

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.

Software for self driving lawn tractor.

Sample-based Quantum Diagonalization: Classically postprocess noisy quantum samples to yield more accurate eigenvalue estimations.

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.

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.

This is a LLM model, where it is tuned to help the farmers for clarifying their doubts about organic farming, with personalised suggestions.

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.

A Framework For Intelligence Farming

Harness the power of machine learning to forecast rice and wheat crop yields per acre in India, aiming to empower smallholder farmers, combat poverty and malnutrition, utilizing data from Digital Green surveys to revolutionize agriculture and promote sustainable practices in the face of climate change for enhanced global food security.

This project aims to design, develop and implement the training model by using different inputs data. The machine will able to learn the features and extract the crop yield from the data by using data mining and data science techniques.

Crop yield Forecasting on the basis of meteorological predictions using some Time series & ML models

AI to Predict Yield in Aeroponics

AI based IoT Smart Farming Solution

Helping Farmers make informed decisions with Machine Learning ! ?‍??

Detecting and segmenting destructive anomalies in farmland from satellite images, improving time, efficiency, and crop yield.

Network Designed to Eventually Yield Sentience and Sapience

Revolutionize your farming with Farmwiser, the ultimate TinyML based Smart Agriculture solution!