This paper explores the transformative potential of artificial intelligence (AI) in the context of sustainable agricultural development across diverse regions in Africa. Delving into opportunities, challenges, and impact, the study navigates through the dynamic landscape of AI applications in agriculture. Opportunities such as precision farming, crop monitoring, and climate-resilient practices are examined, alongside challenges related to technological infrastructure, data accessibility, and skill gaps. The article analyzes the impact of AI on smallholder farmers, supply chains, and inclusive growth. Ethical considerations and policy implications are also discussed, offering insights into responsible AI integration. By providing a nuanced understanding, this paper contributes to the ongoing discourse on leveraging AI for fostering sustainability in African agriculture.

AI expert Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative effects and talks about potential federal legislation to control it. FIRST ON FOX: Lawmakers are eyeing safeguards for integrating artificial intelligence (AI) technology into the U.S.'s agricultural sector. A new bill introduced by Rep. Randy Feenstra, R-Iowa, and backed by both sides of the aisle aims to enforce standards for AI programs connected to everyday Americans' food, fuel and other necessities. Feenstra, whose district is heavily rural, told Fox News Digital that AI is becoming increasingly relevant in the farming industry but that existing guardrails on new technology aren't keeping up with that boom, he suggested. Rep. Randy Feenstra is leading a bill to add safeguards to AI technology used in the agricultural sector (Rod Lamkey/Pool/Getty Images) "From precision agriculture to veterinary software, the latest developments in agricultural technology – including artificial intelligence – have the power to lower input costs for farmers, protect the health of livestock and poultry, and make farming operations more efficient," Feenstra said. "We must be equally active in certifying that these new technologies, products and processes work as they should and uphold the highest industry standards."

The agricultural sector is struggling as fewer and fewer people want to work in this sector, which means that agriculture and horticulture have to consider automation. An increasing number of robots in the agricultural sector are equipped with VBTI's technology. With close to twenty years of experience under his belt, founder Albert van Breemen also helps companies in the manufacturing sector. Albert tells us more about it in this instalment of Start-up of the day. "I have been working on artificial intelligence (AI) since the early 1990s and control engineering. Once I started my studies, I soon came across what is now called Deep Learning. During my time as a business developer at ASML, there was all this hype surrounding artificial intelligence. I heard from a lot of people that we had missed the boat. Everything to do with AI was already being done in America and China. We were supposedly lagging behind with that technology. However, in my mind, we had not missed the boat at all; there's just another one on the way. At some point I received a question from a customer. That customer wanted to scale up their production, but every product had to be checked by hand. That takes an enormous amount of time, and even finding the right people to do this kind of inspection work was almost impossible. That's where Deep Learning enters the picture, for instance, with the help of smart camera systems. And that's how I came up with the idea of turning it into a business. Eventually, in 2018, I decided to start my own company in the field of Deep Learning – which is used within the High Tech Industry."

Being one of the key sectors of a country, the agricultural sector is encouraging the hi-tech industry to introduce different modern applications through major disruptive technologies with the combination of robotics and artificial intelligence. Farmers are reaping the benefits of these following robotics applications in the global agricultural sector to drive revenue and accelerate the productivity of different high-quality crops through multiple kinds of robots. Multiple tech companies have identified the agricultural sector to introduce farming-special technologies to bridge the gap between productivity and earnings of farmers. Robotics applications are transforming the agricultural sector in recent years and will advance these processes in the tech-driven future.

Technological advances are bringing change to a great number of industries, and the agriculture industry is no exception. Farms are slowly starting to see increased adoption of practices based on technologies such as artificial intelligence, cloud computing, the Internet of Things (IoT), and robotics. The adoption of such technologies into the traditional farming practices as we know them is referred to as smart farming or farm automation. Let's have a look at what farm automation is exactly and how it can help farmers tackle a number of challenges in today's agricultural sector. Farm automation specifically focuses on applying data and information technologies for the optimization of production processes of complex farming systems as well as the quality of the food.

In May 2020, with technical support from the UN FAO, China Agricultural University and Chinese e-commerce platform Pinduoduo hosted a "smart agriculture competition". Three teams of top strawberry growers – the Traditional teams – and four teams of scientific AI experts – the Technology teams – took part in a strawberry-growing competition in the province of Yunnan, China, billed as an agricultural version of the historical match between a human Go player and Google's DeepMind AI. At the beginning, the Traditional teams were expected to draw best practices from their collective planting and agricultural experience. And they did – for a while. They led in efficient production for a few months before the Technology teams gradually caught up, employing internet-enabled devices (such as intelligent sensors), data analysis and fully digital greenhouse automation.

In the agricultural sector, the COVID-19 threatens to lead to a severe food security crisis in the region, with disruptions in the food supply chain and agricultural production expected to contract between 2.6% and 7%. From the food crop production side, the travel bans and border closures, the late reception and the use of agricultural inputs such as imported seeds, fertilizers, and pesticides could lead to poor food crop production performances. Another layer of disruption introduced by the mobility restriction measures is the scarcity of agricultural workers, mainly seasonal workers. The lockdown measures and border closures limit seasonal workers' availability to get to the farm on time for planting and harvesting activities. Moreover, most of the imported agricultural inputs travel by air, which the pandemic has heavily impacted. Such transportation disruptions can also negatively affect the food crop production system. This chapter assesses food crop production levels in 2020 -- before the harvesting period -- in all African regions and four staples such as maize, cassava, rice, and wheat. The production levels are predicted using the combination of biogeophysical remote sensing data retrieved from satellite images and machine learning artificial neural networks (ANNs) technique. The remote sensing products are used as input variables and the ANNs as the predictive modeling framework. The input remote sensing products are the Normalized Difference Vegetation Index (NDVI), the daytime Land Surface Temperature (LST), rainfall data, and agricultural lands' Evapotranspiration (ET). The output maps and data are made publicly available on a web-based platform, AAgWa (Africa Agriculture Watch, www.aagwa.org), to facilitate access to such information to policymakers, deciders, and other stakeholders.

The improvement of computers' capacities, advancements in algorithmic techniques, and the significant increase of available data have enabled the recent developments of Artificial Intelligence (AI) technology. One of its branches, called Machine Learning (ML), has shown strong capacities in mimicking characteristics attributed to human intelligence, such as vision, speech, and problem-solving. However, as previous technological revolutions suggest, their most significant impacts could be mostly expected on other sectors that were not traditional users of that technology. The agricultural sector is vital for African economies; improving yields, mitigating losses, and effective management of natural resources are crucial in a climate change era. Machine Learning is a technology with an added value in making predictions, hence the potential to reduce uncertainties and risk across sectors, in this case, the agricultural sector. The purpose of this paper is to contextualize and discuss barriers to ML-based solutions for African agriculture. In the second section, we provided an overview of ML technology from a historical and technical perspective and its main driving force. In the third section, we provided a brief review of the current use of ML in agriculture. Finally, in section 4, we discuss ML growing interest in Africa and the potential barriers to creating and using ML-based solutions in the agricultural sector.

Food production is a critical activity in which every nation would like to be self-sufficient. India is one of the largest producers of food grains in the world. In India, nearly 70 percent of rural households still depend on agriculture for their livelihood. Keeping farmers happy is particularly important in India as farmers form a large vote bank which politicians dare not disappoint. At the same time, Governments need to balance the interest of farmers with consumers, intermediaries and society at large. The whole agriculture sector is highly information-intensive. Even with enormous collection of data and statistics from different arms of Government, there continue to be information gaps. In this paper we look at how Tantra Social Information Management Framework can help analyze the agricultural sector and transform the same using a holistic approach. Advantage of Tantra Framework approach is that it looks at societal information as a whole without limiting it to only the sector at hand. Tantra Framework makes use of concepts from Zachman Framework to manage aspects of social information through different perspectives and concepts from Unified Foundational Ontology (UFO) to represent interrelationships between aspects. Further, Tantra Framework interoperates with models such as Balanced Scorecard, Theory of Change and Theory of Separations. Finally, we model Indian Agricultural Sector as a business ecosystem and look at approaches to steer transformation from within.

Agriculture worldwide is a US $5 trillion industry. And artificial intelligence (AI) is revolutionizing this industry every step of the way -- from preparing soils and sowing seeds to getting products to the kitchen table. AI-powered technologies are increasing productivity and reducing costs significantly throughout the production and supply chain. The market value of global AI in the agricultural sector is currently estimated at $852.2 million. In the next decade alone this value is expected to grow more than 10 times, exceeding $8 billion annually.