An engineer working for Japanese carmaker Nissan has built a robot to help farmers reduce the use of herbicides and pesticides on their rice crops. The compact robot, called Aigamo, is designed to mimic the natural use of ducks that paddle around in flooded paddy fields. Ducks have been used as natural weed repellents for centuries to tear them up and feed on insects, with their manure even acting as an additional fertiliser. As it glides through the water, two mechanisms on the bottom muddy the water to prevent weeds from getting enough sunlight to grow. The technique was used in the late 20th century with live ducks, called'aigamo,' which would paddle the water with the same results and eat any insects they found along the way.
During a wide-ranging discussion at Amazon's re:MARS conference in Las Vegas, Naveen Rao, corporate vice president and general manager of AI at Intel, spoke about machine learning's rapid progress and the fields it might transform, in addition to the steps he believes must be taken to ensure it's not abused. Rao compared the advent of modern AI approaches with the iPhone. Like the iPhone, he said, machine learning -- a technique underlying systems from Amazon's Alexa to Google Lens -- wasn't the first form of AI, but it was nonetheless "exciting" and "consequential." He characterizes the coming AI revolution as the single largest transition the human species has ever encountered. "Few people anticipated the big-picture changes that smartphones would bring. No one foresaw that smartphones could make our work day substantially longer because we'd never get away from email," he said.
The 3rd AI for Good Global Summit, a leading United Nation platform for multilateral dialogue on Artificial Intelligence (AI), was kicked off in Geneva, Switzerland, May 28-31. Bringing together over 1,200 interdisciplinary participants from 200 countries, the AI for Good Global Summit connects AI innovators with problem owners to identify practical applications of AI to accelerate process towards the United Nations Sustainable Development Goals (SDGs). Speakers from industry giants such as Microsoft, Google, Mastercard, IBM, Airbus, Siemens, Danone and Roland Berger were present at the Summit. "Zero Hunger" is one of the 17 UN SDGs expected to be achieved by 2030. According to the United Nations, up to 80% of food consumed in most developing countries are produced by smallholder farmers who, however, account for approximately 50% of the 815 million people suffering from hunger worldwide.
Much of modern-day agriculture is dominated by monoculture, the practice of producing a single crop on a large swath of land. This approach makes it easier for farmers to manage their fields with tractors and other basic automated tools, but it also strips the soil of nutrients and reduces its productivity. As a result, many farmers rely heavily on nitrogen-based fertilizers, which can convert into nitrous oxide, a greenhouse gas 300 times more potent than carbon dioxide. Robots run on machine-learning software could help farmers manage a mix of crops more effectively at scale, while algorithms could help farmers predict what crops to plant when, regenerating the health of their land and reducing the need for fertilizers.
"Zero Hunger" is one of the 17 UN SDGs expected to be achieved by 2030. According to the United Nations, up to 80% of food consumed in most developing countries are produced by smallholder farmers who, however, account for approximately 50% of the 815 million people suffering from hunger worldwide. At the Summit's session on AI and Agriculture, Justin Gong, Co-founder and Vice President of XAG, together with other panel experts from Microsoft, Tata Group and Connecterra has proposed projects and initiatives to exploit new possibilities of AI technology to improve food security and end hunger. Artificial Intelligence, through continuously analysing massive data related to climate, lands, crop growing, etc., while automatically designing and optimising algorithms for decision-making, can help farmers diagnose plant diseases, predict natural disasters and employ appropriate resources to close the yield gap. At XAG, AI-powered intelligent devices such as drones and sensors have been leveraged to establish digital farming infrastructure in rural areas and enable precision agriculture which, for example, accurately target pesticides, seeds, fertilisers and water to wherever it is needed.
Gone are the days of turning stones into spears. With the advent of new technologies, we've learned to develop tools that not only make living faster and easier every day, but also improve the future of humanity as a whole. Today's Chart of the Week draws from the MIT Technology Review, which features Bill Gates' predictions for the top 10 breakthrough inventions that will capture headlines in 2019. Companies would see measurable benefits, with just one breakthrough here garnering a 5% jump in productivity. Sanitation that doesn't require sewers would not only prevent exposure diseases but also help turn waste into useful products like fertilizer.
In developing countries like India agriculture plays an extremely important role in the lives of the population. In India, around 80\% of the population depend on agriculture or its by-products as the primary means for employment. Given large population dependency on agriculture, it becomes extremely important for the government to estimate market factors in advance and prepare for any deviation from those estimates. Commodity arrivals to market is an extremely important factor which is captured at district level throughout the country. Historical data and short-term prediction of important variables such as arrivals, prices, crop quality etc. for commodities are used by the government to take proactive steps and decide various policy measures. In this paper, we present a framework to work with short timeseries in conjunction with remote sensing data to predict future commodity arrivals. We deal with extremely high dimensional data which exceed the observation sizes by multiple orders of magnitude. We use cascaded layers of dimensionality reduction techniques combined with regularized regression models for prediction. We present results to predict arrivals to major markets and state wide prices for `Tur' (red gram) crop in Karnataka, India. Our model consistently beats popular ML techniques on many instances. Our model is scalable, time efficient and can be generalized to many other crops and regions. We draw multiple insights from the regression parameters, some of which are important aspects to consider when predicting more complex quantities such as prices in the future. We also combine the insights to generate important recommendations for different government organizations.
Environmental stresses such as drought and heat can cause substantial yield loss in agriculture. As such, hybrid crops which are tolerant to drought and heat stress would produce more consistent yields compared to the hybrids which are not tolerant to these stresses. In the 2019 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the yield performances of 2,452 corn hybrids planted in 1,560 locations between 2008 and 2017 and asked participants to classify the corn hybrids as either tolerant or susceptible to drought stress, heat stress, and combined drought and heat stress. As one of the winning teams, we designed a two-step approach to solve this problem in an unsupervised way since no data was provided that classified any set of hybrids as tolerant or susceptible to any type of stress. First, we designed a deep convolutional neural network (CNN) that took advantage of state-of-the-art modeling and solution techniques to extract stress metrics for each type of stress. Our CNN model was found to successfully distinguish between the low and high stress environments due to considering multiple factors such as planting/harvest dates, daily weather, and soil conditions. Then, we conducted a linear regression of the yield of hybrid against each stress metric, and classified the hybrid based on the slope of the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress. Our results suggested that only 14 % of the corn hybrids were tolerant to at least one type of stress.
Agerris, an Australia-based robotics and AI platform for agriculture, announced over the weekend that it has raised $6.5 million (AUSD) in seed funding from Uniseed, Carthona Capital and BridgeLane Group. The startup was founded by Professor Salah Sukarrieh and began as research at the Australian Centre for Field Robotics at the University of Sydney (which is also a partner in Uniseed). From the looks of it, Agerris is building a modular robotics and AI platform that has broad applications for both plant and livestock farmers. According to a University of Sydney news post, Agerris has two main products. The "Swagbot" can autonomously monitor and identify weed issues, detect food and crops through computer vision, as well as herd livestock.
How Artificial Intelligence can help with Biosecurity. Around the globe, different parts of the food supply chain are contaminated on a daily bases. Biosecurity, as defined by the Food and Agriculture Organization of the United Nations is the strategic and integrated approach to manage risks in food safety, animal and plant life and health, and biosafety. It relates to policy and regulatory framework that improves food health inside different points in the global food supply chain. China, a country that consumes more pork per capita than any other country after Vietnam, is contending with a deadly case of African Swine Fever.