As part of the 2022 Digital Economy Summit, the U.S. Chamber of Commerce’s U.S.-Africa Business Center (USAfBC) in conjunction with AmChams across Africa, is hosting its annual Digital Competition for African Startups. The 2022 Africa Digital Innovation Competition had 1706 entries and 563 Submissions from 50 countries. VAIS has been selected the Regional Champion for North Africa and one of the top 10 continental finalists in the 2022 Africa Digital Innovation Competition. During the competition, VAIS presented its ground-breaking Virtual Field Probing (VFP) technology-based irrigation intelligence and crop analytics products highlighting the significant positive impact this technology will have on farmers in Africa and across the world.
We are happy to announce that VAIS was selected to join the first cohort of Plug and Play Africa accelerator program in Egypt. The 3-month program starts June 2022 and is supported by USAID and Information Technology Industry Development Agency, ITIDA.
VAIS announces the release of Virtual Field Probing (VFP) technology, its patent-pending satellite-enabled sensing technology. VFP applies advanced artificial intelligence models to the abundance of under-utilized raw satellite data produced by tens of satellites in order to gain high-level insights, facilitate reliable predictions, and empower new applications. More information on VFP technology can be found here.
The potato is the third most important food crop in the world after rice and wheat in terms of human consumption. However, potatoes are vulnerable to a variety of diseases and pests. They are also very susceptible to water stresses. This whitepaper describes how VAIS deep learning-based crop analytics were deployed to monitor two center pivot irrigated potato fields in the region of Wadi Al-Natrun in northern Egypt where field stress regions were computed on a daily basis for micro zones with area of 3x3 square meters. Manual inspection by expert agronomists of the stress zones discovered by CropROBO has concluded that nearly 100% of the biotic and abiotic stresses at the above resolution were detected including those not observable by ground-level inspection. More information in the paper .
We are pleased to announce that VAIS delegate will be present at the World Agri-Tech Innovation Summit in San Francisco from 22 to 23 March 2022. The Summit is the annual meeting place for the global agri-tech ecosystem with growers, agribusiness leaders, technology pioneers and investors coming together to exchange insights, be inspired, and identify future partners. During this event, we will showcase our latest crop and irrigation analytics technologies to customers, launch partners and potential investors. To arrange for a meeting during this event, please send email to: firstname.lastname@example.org
Notwithstanding 800+ applicants, an extremely competitive selection process, and an acceptance rate of 1.3%, VAIS was successfully selected as one of the 10 companies to join Flapmax FAST Accelerator in Spring 2022 cohort. The program is supported by Microsoft who is creating partnerships with accelerators and incubators to provide markets, technical skills and investment opportunities to African startups (more info) . It will focus on empowering selected companies to build sustainable businesses at global scale through a carefully curated course, cloud credit with Azure, access to VC funding, and market expansion with Microsoft.
In this Arabic-language blogpost, an independent journalist writes about why VAIS was one of the 2 Egyptian companies (10 in total) selected to pitch in Africa Tech Summit 2022. The post provides information on the company’s vision, values, technologies, and achievements. The article can be found here. (Disclaimer: VAIS is an NU spinoff and not owned by NU)
We are pleased to announce that VAIS got selected as one of the leading African startups to pitch live at the Africa Tech Summit 2022 in Nairobi, Kenya on February 24th. The pitch, titled “FarmGATE: Welcome to Agriculture 4.0”, will summarize how the crop and irrigation analytics provided in FarmGATE utilize artificial intelligence and remote sensing data to transform old farming practices and address challenges facing the agriculture sector including climate change, farmland degradation, and inaccessibility to precision farming technology.
To address current deployment requirements, VAIS has introduced Arabic language support in FarmGATE, its all-in-one digital platform. This will also help keep up with client demands for local language support in the Middle East and North Africa (MENA) region. Support for other languages including Swahili will soon be introduced in all of VAIS AgriTech products.
VAIS will be exhibiting at the RiseUp Summit ‘21, MENA region's largest innovation and entrepreneurship summit. We will be there on 25 and 26 November 2021 to meet potential investors, future stakeholders, and anyone else who has an interest in our technologies. We will also showcase our recent AI-powered AgriTech and Earth Observation analytics. Please come and meet us, we love to multiply efforts.
We are pleased to announce that VAIS got accepted in a Google for Startups program. The Startup Advisor: Sustainable Development Goals (SDG) program aims at empowering social impact startups through cross-sector collaboration with the goal to fast track the United Nations SDGs 2030 agenda. VAIS is proud to target three SDGs (Zero Hunger, Responsible Consumption and Production, Life on Land) through its AI-based precision agriculture products. Through this program, we will receive support and mentorship from Google in order to improve our product features and scale our operations overseas.
East Owainat (Ar: Sharq El Owainat) is located in Egypt’s Western Desert, in the south-west of the country, between the River Nile and Libya. The area is home to a number of large agricultural companies, both national and regional, producing a variety of crops including strategic crops, medicinal crops and fruits/vegetables. The crop circles shown in the image are a result of center-pivot irrigation systems used in this area. VAIS FarmGATE will be deployed in several wheat farms in the 2021/2022 winter season. Using data from satellite imagery and land observations, FarmGATE will provide continuous intelligent monitoring of crop growth and vigor, detection/diagnosis of biotic/abiotic stresses, and early warning against adverse weather. It will also be used for irrigation monitoring including recommendations, detection of water stress regions, and reporting of damaged pipes and problematic irrigation systems.
Head of AI/ML at VAIS presented the "Locational Offset Correction" project at the European Space Agency Φ-WEEK 2021. The project, which uses deep learning to correct the location of maize field centers given satellite imagery of the field, is a joint collaboration between VAIS, Zindi and CGIAR and is funded by the Lacuna initiative. The Φ-WEEK 2021 event focused on accelerating the future of Earth Observation (EO), presenting recent developments and bold and transformative ideas in EO Open Science.
VAIS FarmGATE is a comprehensive digital platform that covers the full spectrum of services needed for AI-powered precision agriculture. FarmGATE will provide growers with unprecedented features to optimize agricultural inputs, alleviate the impact of stresses, manage their farms better, and maximize yield, all in the palm of their hands. It will also include innovative in-field scouting guidance, AR-based localization and integration with IoT field sensors. FarmGATE is available to download on Play Store (App Store coming soon) as a subscription-based service for major growers and ag businesses. For inquiries, please contact email@example.com
VAIS’s key role in the Smart Agricultural Clinic (SAC) is development of the Plant Smart Analytics Engine (PSAE) module that can detect and diagnose biotic and abiotic stresses of four strategic crops (wheat, rice, maize, barley). Current PSAE module caters for the wheat crop where it achieves accuracy of >97% in diagnosing wheat stresses including yellow rust, powdery mildew, insects (aphid, weevil), and nitrogen deficiency, among others. The PSAE module utilizes advanced deep learning models on stressed and normal images of wheat leave, stem and head images to achieve a high accuracy that is expected to progressively increase further as more images are acquired during system operation.
Karim Amer, Head of AI/ML at VAIS, became the first Kaggle Master of Data Science Competitions from Egypt after achieving a silver medal in “SETI Breakthrough Listen - E.T. Signal Search” challenge. Kaggle is the largest online platform of machine learning and data scientists where only ~1% hold the Kaggle Master title (less than 10 from MENA countries). Congratulations Karim!
We are proud to be part of the Smart Agricultural Clinic (SAC), a national initiative aiming to use technology to deliver personalized agriculture extension and veterinary services to farmers and breeders all over Egypt. The SAC project has been featured in several prominent local and regional newspapers including Al-Masry Al-Youm, Agri2Day, and Al-Ahram, among others. VAIS is the project partner responsible for developing computer vision and deep learning algorithms for diagnosis of crop and animal diseases using more than 6,000,000 images. VAIS is also delivering data collection protocols, on-site training, data annotation tools, and cloud-based analytics engine implementation.
VAIS announces the release of its open-source Planet Box Extractor Python API which enables the computation of bounding boxes of interest from satellite images. Extraction of bounding boxes from spherical earth surface imagery requires special mathematical treatment which the API provides. It can be used to download satellite images from Planet Labs (especially for open NICFI data), by stitching Mercator tiles and cropping around target locations with specified area and zoom level. A Medium blog post is also provided to describe the concepts behind this work.
A team of VAIS scientists including Karim Amer and Kareem Eissa achieved the fourth place overall among 622 contesting international teams at MagNet: Model the Geomagnetic Field competition hosted on DrivenData. The input data for this challenge is composed of solar wind measurements collected from two satellites: NASA's Advanced Composition Explorer (ACE) and NOAA's Deep Space Climate Observatory (DSCOVR). The goal is to predict the Disturbance Storm-Time Index (DST), a measure of magnetic activity, from the provided data. The competition is sponsored by NOAA: National Oceanic & Atmospheric Administration with support from NASA -National Aeronautics and Space Administration.
VAIS signs a contract with Zindi (a rising international data science competition platform) to collaborate along with CGIAR (the Consultative Group for International Agricultural Research) on a Lacuna funded project. The project aims to provide machine learning solution for correcting location errors, which are a common problem in agriculture data sets used for yield estimation. The project is expected to have a positive impact on smallholder farmers especially in several African countries including Kenya and Rwanda.
Visual and AI Solutions (VAIS) signs incubation agreement with NilePreneurs based on which the company will be hosted within NU Tech Space premises and provided with financial and mentoring support. NilePreneurs is a national, fast growing initiative funded by Central Bank of Egypt with the aim to enhance the ecosystem for entrepreneurs & SMEs in Egypt.
Tropical storms and hurricanes can inflict huge damage on human life and personal property which makes early tracking and prediction of such dangerous phenomenon a vital task. Therefore, Radiant Earth and NASA (among other elite organizations) convened on a competition to predict tropical storm speed using satellite imagery. VAIS’s applied R&D team, lead by K. Amer, proposed a novel AI-based solution using multi-step Convolutional Neural Networks. Our solution reduced the error of state-of-the-art models by more than 25% and achieved the second place overall among 733 contesting international teams.
The Smart Agricultural Clinic (SAC) project aims to solve the problem of lack of agricultural extension services in Egypt that is mainly affecting small-scale/smallholder farmer segments with weak financial capacity. The project is funded by the Academy for Scientific Research and Technology and will be implemented by a consortium comprising the Agriculture Research Center, Nile University, Orange Labs, and VAIS. Once finished, SAC will provide an integrated end-to-end digital system to deliver personalized agriculture extension and veterinary services to tens of thousands of farmers and breeders. VAIS key role in the project will focus on development of data collection and annotation tools, AI-based plant and animal disease detection and diagnosis analytics engines as well as optimization, packaging and deployment of AI algorithms on the cloud.
VAIS announces the release of its Real-Time 3D Detection and Segmentation Engine. The engine uses multi-modal data acquired from Light Detection and Ranging (LiDAR) or depth camera sensors to provide real-time robust semantic segmentation of agricultural scene. The engine is used in fruit counting, crop yield estimation and plant growth quantification as well as for enhanced detection and navigation capabilities for agricultural robots.
Our abstract on satellite imagery analysis using deep learning is accepted for presentation during the FALL 2020 meeting of the prestigious American Geophysical Union (AGU). The talk will be in the “End-to-End Machine Learning for Earth Science: Tools, Frameworks, and Practical Applications” session which will be held on Tuesday, 8 December 2020.
VAIS scientists won a Data Science Africa (DSA) 2020 Award. The awards are given to research projects that leverage data science, artificial intelligence (AI) and machine learning (ML) methods to develop tools with broad societal benefits. The project will use advanced imaging modalities and deep learning analytics for plant disease detection and diagnosis. The fund is provided by Facebook Inc. and managed by DSA.
VAIS partners with CGIAR (the Consultative Group for International Agricultural Research) and One Acre Fund (OAF) in Lacuna fund application to create a multi-modal dataset to be used for African maize yield estimation. The project will be implemented in East Africa with Rwanda being the country of focus. VAIS will provide automatic detection and segmentation technologies for RGB images and 3D point clouds generated from laser scanning of maize fields. We will also develop computer vision and deep learning algorithms for computing multi-stage yield estimates as well as the required annotation tools.