Artificial intelligence has invaded the agriculture field during the last few years. From automatic crop monitoring via drones, smart agricultural equipment, food security and camera-powered apps assisting farmers to satellite imagery based global crop disease prediction and tracking, computer vision has been a ubiquitous tool. This workshop aims to expose the fascinating progress and unsolved problems of computational agriculture to the AI research community. It is jointly organized by AI and computational agriculture researchers and has the support of CGIAR, a global partnership that unites international organizations engaged in agricultural research for a food-secure future.
Computer Vision for Agriculture (CV4A) is the second workshop of the Computer Vision for Global Challenges initiative and will focus on agriculture. It will be held in April 2020, in conjunction with the International Conference on Representation Learning (ICLR), in Addis Ababa, Ethiopia. It will be a full-day event and will feature invited speakers, poster and spotlight presentations, a panel discussion and (tentatively) a mentoring/networking dinner. It will also feature two open challenges, both hosted on the African platform Zindi:
Below are some first information and dates regarding virtual presentations and camera ready submissions for authors of accepted papers.
Video Presentations (videos due April 12th)
Camera-ready instructions (upload on arXiv before April 8th)
Crop disease has threatened the global food supply since the beginning days of agriculture, over 10,000 years ago. Wheat, along with maize and rice, represent the three most important crops to humanity. One of the most devastating plant disease is a fungal infection of wheat, called rust. A particularly virulent strain of wheat rust known as Ug99 has emerged from Uganda, with up to 100% yield losses from infected fields. Ug99 has spread across Africa and is moving into South Asia, threatening regional food security. The international agricultural community is focusing on methods to monitor and track the presence and spread of Ug99 across the world. Different strains of wheat rust can be initially identified by visual symptoms with the naked eye. The challenge is to develop a computer vision-based (CV) detection solution for differentiating strains of wheat rust which can be deployed around the world. If accurate CV models can be generated, these models can be deployed across the network of wheat farmers, breeders, and pathologists around the world to track the emergence and spread of wheat rust, identify promising resistant varieties, and to disseminate resistant varieties to millions of farmers all over the world.
Earth Observations provide invaluable data across different spatio-temporal scales enabling applications for agricultural monitoring. Meanwhile, machine learning (ML) techniques can be utilized to advance these applications, and develop faster, more efficient and scalable models. In this challenge, a training dataset of crop types in Kenya will be provided and participants are asked to develop the best model to predict crop types using mutli-spectral and radar data from Sentinel-2 and Sentinel-1 satellites. The data includes a crop type label as well as time series of measurements from each of the satellites during the growing season. Training dataset for this challenge is developed from ground reference data collected by Plant Village team in the year 2019. Radiant Earth Foundation in partnership with Plant Village has generated the training data, that will be hosted on Radiant MLHub with an API that all participants can easily access.