Agriculture is the oldest industry known to humanity and a key driver in human evolution. Multiple revolutions have shaped agricultural productivity through automation and information availability. Digital technology further enhances agriculture through drones and other robotic automation, satellite-based weather, land use, water, and crop information, IoT-based intelligent farm management, hydroponics, and vertical farming to improve space utilization, and intelligent algorithms and data sharing to optimize lifecycles.
Linux Foundation announced the launch of AgStack Foundation, the world’s first open source digital infrastructure project for agriculture. AgStack Foundation will scale across the global agriculture ecosystem and encompass frameworks across infrastructure, data, AI, security, geospatial utilities, mobility and provide a platform for AgriTech innovation.
While AgStack will drive digital open source innovation in agriculture in the near future, did you know there already are over 1,700 popular libraries readily available for agriculture related technologies? kandi collection for AgriTech Solutions samples the most popular across diverse use cases in agriculture such as community and lookup services, farm management; stand alone automation like robotic tractors, irrigation; predictive algorithms for use cases like soil moisture, crop yield prediction, fertilizer requirements; data sets, and commercial operations.
You can start with simple community and lookup services such as OpenFarm by openfarmcc, trefle-api by treflehq, Automatic-leaf-infection-identifier by johri002. Try farm management and uses cases like organic certification through tania by Tanibox, tania-core by Tanibox, farmOS by farmOS, ekylibre by ekylibre, FarmData2 by DickinsonCollege. For simpler, stand alone automation across tractors, watering try lawn_tractor by ros-agriculture, Lawn-mower-robot by steger123, GardenPi by rjsears. For larget scale automation, try openag-device-software by OpenAgricultureFoundation, FruxePi by fruxefarms, SuperGreenOS by supergreenlab, KAISPE_Agriculture_Remote_Monitoring by KAISPE_LLC. If you are looking for specific information and predictive algorithms for use cases like soil moisture, crop yield prediction, fertilizer requirements try pycrop-yield-prediction by gabrieltseng, harvest_helper by damwhit, ML-Precision-Agriculture-Web-App by Empharez, Smart-Agriculture-using-IoT-and-Machine-Learning by Chinukapoor, Smart-Farming-Fertilizer-Prediction by suvam14das, kisanmitra by ashishpatel0720, WaporTranslator by TimHessels, crops_and_oscillations by matheino. For data sets to train your models, you can use agridat by kwstat, agridatasets by picasa. For commercial operations like e-commerce and insurance, try CropInsuranceSolution by sachinjegaonkar, localorbit by LocalOrbit, Agri-Sasa by bensalcie, E-Mandi by madhurpatle.