Figure has developed a full-body humanoid robot, Figure-01, that can walk, talk and interact. Le Van Tam is no stranger to how the vagaries of global trade can determine the fortunes of small coffee farmers like him. He first planted coffee in a patch of land outside Buon Ma Thuot city in Vietnam's Central Highland region in 1995. For years, his focus was on quantity, not quality. Tam used ample amounts of fertilizer and pesticides to boost his yields, and global prices determined how well he did.

Crop management decision support systems are specialized tools for farmers that reduce the riskiness of revenue streams, especially valuable for use under the current climate changes that impact agricultural productivity. Unfortunately, small farmers in India, who could greatly benefit from these tools, do not have access to them. In this paper, we model an individual greenhouse as a Markov Decision Process (MDP) and adapt Li and Li (2019)'s Follow the Weighted Leader (FWL) online learning algorithm to offer crop planning advice. We successfully produce utility-preserving cropping pattern suggestions in simulations. When we compare against an offline planning algorithm, we achieve the same cumulative revenue with greatly reduced runtime.

A farmer in Niger tends to a tree sprout growing among his millet crop.Tony Rinaudo/World Vision Australia This story was originally published by Yale Environment 360 and is reproduced here as part of the Climate Desk collaboration. For decades, there have been reports of the deforestation in Africa. And they are true--the continent's forests are disappearing, lost mainly to expanding agriculture, logging, and charcoal-making. Maybe not, according to new satellite data analyzed by artificial intelligence and a growing body of on-the-ground studies. This new research is finding ever more trees outside forests, many of them nurtured by farmers and sprouting on their previously treeless fields.

Earlier this year, the Indian government issued new guidelines allowing private entities to easily use, create, and access land data instead of going through long clearance protocols. The newly available data includes location information about physical structures, boundaries, natural phenomena, weather patterns, and more, gathered through ground-based survey techniques, photogrammetry using drones, lidar, radar, and so on. On paper, this means a green light for small- and medium-sized companies to collect and use this data to build commercial applications and services related to mapping. It is also a relief to alternative or participatory mapping communities, such as counter-mapping initiatives (in which local, indigenous populations make their own maps in their own contexts), which have so far lurked in a gray area of legality. For the development and academic sectors, too, it heralds greater access to maps and related data for research.

California, US-based, SRI International today announced that its insurtech venture spinout, Wingsure, is expanding its artificial intelligence (AI) and augmented reality (AR) capabilities to deliver instant access to personalised insurance products for underserved small farmers and communities worldwide. The initial implementation will focus on India where 50 per cent of households are dependent on agriculture for their living. Many are located in remote rural locations without access to financial services and are unable to insure against crop failure or other unexpected events. Wingsure's mobile platform provides previously inaccessible services to this enormous market, where 600 million people are agri dependent. Wingsure is an insurtech platform that revolutionises how small farmers and rural customers leverage insurance and financial products to transform their lives and livelihood.

How will artificial intelligence (AI) affect EU farmers? Bernard Ader, vice president of Copa-Cogeca, the united voice of European Farmers and Agri-cooperatives, answers Future Farming's questions. "It is essential for cooperatives and their farmer-owners to clearly understand the potential benefits and risks involved." What can European farmers expect from EU policies on AI? And in which areas does AI benefit farmers?

We are not aware of any concrete examples of AI already being used in farming practice, but the introduction of artificial intelligence provides the power to process huge amounts of data, pooling, and exchanging information with multiple data sources. It also provides decision support systems for complex choices that farmers and their cooperatives need to make. This gives farmers and their cooperatives a powerful tool to yield significant gains in terms of efficiency and productivity. It will be key to handle essential repetitive and diverse agricultural tasks such as weeding, harvesting crops, or milking cows. The same goes for the processing facilities for packaging logistics handled by our cooperatives. Artificial Intelligence also has a positive impact on working conditions, as it helps optimise the labour process and helps in accompanying farmers which can be significant for our sector. The same thing goes for farm and enterprises safety. We see that artificial intelligence can also support us in overcoming these huge problems. Additionally, AI can support us in tackling environmental and climatic challenges, especially in reducing the impact on the environment, reducing our carbon-footprint, and improving the functioning of the value chain. Agri-food cooperatives increasingly face the challenge of sustainable production. We are investing to improve the scope of innovations, preserve the integrity of the ecosystem, and improve the use of natural resources.

Agritech in India has seen a skyrocketing growth with numerous startups emerging with new technologies and advanced methods such as data analytics, machine learning and satellite imaging, among others to cater to the needs of Indian farmers and maximise their output. India with 118.7 million farmer households, accounting for more than half of the population is heavily dependent on agriculture as a primary source of income. But Indian agriculture is plagued by several problems both man made and natural such as; unavailability of seeds, small and fragmented land-holdings, problems with irrigation due to uncertain monsoon, shortage of finance among other necessities, leaving farmers helpless and with no option but to let their produce go at dirt cheap prices. Therefore, Agritech is clearly one of the most needed industries in India and here is a list of top 9 agritech startups helping Indian farmers by providing agronomic intelligence. Started in 2016 by Nishant Vats and Tauseef Khan, Gramophone is a one-stop e-commerce platform for farmers delivering agricultural inputs in more than 10,000 villages.

"Qeexo’s Runtime-Free Architecture for Efficient Deployment of Neural Networks on Embedded Targets" Rajen Bhatt Director of Engineering Machine Learning, Qeexo Co Neural networks, including convolutional, feed-forward, recurrent, and convolutional-recurrent, are increasingly popular due to their recent successes in AI applications. Developing neural network models for tinyML applications can be very cumbersome due to constraints of embedded targets having low-power MCUs. Qeexo has developed a runtime-free architecture for efficiently converting TensorFlow-and-PyTorch-generated models to target libraries. This approach builds models which are orders of magnitude smaller than TensorFlow Lite Micro and does not compromise on latency or inference performance. "Democratization of Artificial Intelligence (AI) to Small Scale Farmers - a framework to deploy AI Models to Tiny IoT Edges that operate in constrained environments" Chandrasekar Vuppalapati Senior Vice President - Products & Programs Hanumayamma Innovations and Technologies Inc. Big Data surrounds us. Every minute, our smartphone collects huge amounts of data from geolocations to the next clickable item on an ecommerce site. Data has become one of the most important commodities for individuals and companies. Nevertheless, this data revolution has not touched every economic sector, especially rural economies, e.g., small farmers have been largely passed over the data revolution, in the developing countries due to infrastructure and compute constrained environments. Not only isthis a huge missed opportunity for big data companies, it is one of the significant obstacles in the path towards sustainable food and a huge inhibitor closing economic disparities. The purpose of the talk is to present the TinyML framework to deploy artificial intelligence models in constrained compute environments that enable remote rural areas and small farmers to join the data revolution.

The 62-year-old owns 52 acres of land on which he grows rice, cotton, pulses, sugarcane and black gram. For years, pests had been attacking his black gram crop, and it took days, even weeks, to consult agricultural experts. By the time he got the remedies, the infection spread, resulting in crop loss. Last year, just as he spotted shrunken leaves, he downloaded an artificial intelligence (AI)-driven application on his phone and uploaded photographs of the leaves. The app, Plantix, took minutes to diagnose that the crop had crinkle virus infection and suggested remedies. The disease, if detected early, is easily controllable by tackling aphid, small sap-sucking insects that act as vectors for the virus. "The disease was diagnosed in two minutes. I started remedial measures that afternoon itself. I also used better irrigation methods and harvested 850 kg black gram per acre, all thanks to AI," says Ravichandran. The earlier output used to be 150 kg per acre. In 2017, Maharashtra, the country's biggest cotton producer, was hit by its worst pest infection on cotton in recent times.