Digital agriculture offers one of the most promising approaches to address the challenge of sustainably increasing food production by 70% by 2050 (from 2010 production levels). Using the latest advances in artificial intelligence (AI) and machine learning (ML), the farmer can be empowered with predictions that can improve farm processes, from planning until harvest.
\nSatellite data and remote sensing techniques can provide agricultural insights, by using advanced image processing algorithms and AI algorithms on multiple spectral bands in satellite imagery to estimate crop health. However, satellite imagery alone is unable to capture all the data from the farms. Recent work has investigated the use of in-field sensors and imagery to complement satellite data, along with unmanned aerial vehicles (UAVs), cameras and sensors on tractors. These data are streamed to the cloud using the latest Internet of Things (IoT) technologies, where they are processed to provide valuable insights to the farmer.
\nHowever, there are two key challenges in enabling this IoT-enabled vision of data-driven farming. First is the ability to get data from the farm, as most farms have poor Internet connectivity. The second is how to make data from different modalities actionable by the farmers. The heterogeneous sensor streams need to be merged and analysed together with satellite data. In addition, data collection and analysis need to be done in a way that does not add to the farmer\u2019s workload, but instead streamline efficiency.
\nThe FarmBeats solution at Microsoft uses new technologies, such as TV white spaces and Azure IoT Edge, to collect large amounts of data from the farm via sensors, tractors, cameras, drones and other devices. FarmBeats then develops new AI & ML algorithms (trained on this data), along with any available remote sensing data, to provide unique, actionable insights to farmers which can improve productivity.<\/p>\n","protected":false},"excerpt":{"rendered":"
Digital agriculture offers one of the most promising approaches to address the challenge of sustainably increasing food production by 70% by 2050 (from 2010 production levels). Using the latest advances in artificial intelligence (AI) and machine learning (ML), the farmer can be empowered with predictions that can improve farm processes, from planning until harvest. Satellite […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-500201","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"Australian Farm Institute","msr_edition":"Farm Policy Journal","msr_affiliation":"","msr_published_date":"2018-08-09","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"7 -- 16","msr_chapter":"2","msr_isbn":"","msr_journal":"Farm Policy Journal","msr_volume":"15","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"http:\/\/www.farminstitute.org.au\/publications\/journal\/farm-policy-journal-winter-2018?platform=hootsuite","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"http:\/\/www.farminstitute.org.au\/publications\/journal\/farm-policy-journal-winter-2018?platform=hootsuite","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/www.farminstitute.org.au\/publications\/journal\/farm-policy-journal-winter-2018?platform=hootsuite"}],"msr-author-ordering":[{"type":"user_nicename","value":"Ranveer Chandra","user_id":33344,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Ranveer Chandra"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[768895],"msr_project":[881235,239387],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":881235,"post_title":"Project FarmVibes","post_name":"project-farmvibes","post_type":"msr-project","post_date":"2022-10-06 08:00:00","post_modified":"2024-07-29 09:55:45","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-farmvibes\/","post_excerpt":"Democratizing digital tools for sustainable agriculture As one of the biggest contributors to climate change, agriculture, along with land use degradation and deforestation, account for about a quarter of the global GHG emissions and consumes about 70% of the world\u2019s freshwater resources. Agriculture is also amongst the most impacted by climate change. Farmers depend on predictable weather for their farm management practices, and unexpected weather events, e.g., high heat, floods, etc. leaves them unprepared to…","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/881235"}]}},{"ID":239387,"post_title":"FarmBeats: AI, Edge & IoT for Agriculture","post_name":"farmbeats-iot-agriculture","post_type":"msr-project","post_date":"2016-06-16 20:10:17","post_modified":"2024-08-29 22:15:59","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/farmbeats-iot-agriculture\/","post_excerpt":"Our goal is to enable data-driven farming. We believe that data, coupled with the farmer's knowledge and intuition about his or her farm, can help increase farm productivity, and also help reduce costs. 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