We are going to need the IoT (Internet of Things) to help solve the problems that are facing agriculture and the future of food. There is not a person on the planet that doesn't understand the importance of food, ag, and farming. Couple these facts with people living longer than ever, and we just keep having babies and you have a pot ready to boil over. All of these factors combined will lead to a more crowded planet than we've ever experienced before. With more mouths to feed, we as a global society will need to figure out how to produce more food.
Recently we have discussed the emerging concept of smart farming that makes agriculture more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is Machine Learning -- the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. Machine learning is everywhere throughout the whole growing and harvesting cycle. It begins with a seed being planted in the soil -- from the soil preparation, seeds breeding and water feed measurement -- and it ends when robots pick up the harvest determining the ripeness with the help of computer vision.
Thom Golden, senior vice president of data science at Capture Higher Ed, took his family to Chaney's Dairy Barn in Bowling Green, Ky. The experience left him with more than just some premium homemade ice cream and an afternoon of fun in the country. He was able to see firsthand how Artificial Intelligence (AI) can open up new possibilities for a family business. "I've never been a dairy farmer, but I know enough to understand that it's exhausting," Thom says during a recent episode of The Weightlist, Capture's podcast that regularly discusses the areas between data, new technologies and enrollment management. He hosts the podcast with Brad Weiner, director of data science at Capture.
A reliable and accurate forecasting method for crop yields is very important for the farmer, the economy of a country, and the agricultural stakeholders. However, due to weather extremes and uncertainties as a result of increasing climate change, most crop yield forecasting models are not reliable and accurate. In this paper, a hybrid crop yield probability density forecasting method via quantile regression forest and Epanechnikov kernel function (QRF-SJ) is proposed to capture the uncertainties and extremes of weather in crop yield forecasting. By assigning probability to possible crop yield values, probability density forecast gives a complete description of the yield of crops. A case study using the annual crop yield of groundnut and millet in Ghana is presented to illustrate the efficiency and robustness of the proposed technique. The proposed model is able to capture the nonlinearity between crop yield and the weather variables via random forest. The values of prediction interval coverage probability and prediction interval normalized average width for the two crops show that the constructed prediction intervals cover the target values with perfect probability. The probability density curves show that QRF-SJ method has a very high ability to forecast quality prediction intervals with a higher coverage probability. The feature importance gave a score of the importance of each weather variable in building the quantile regression forest model. The farmer and other stakeholders are able to realize the specific weather variable that affect the yield of a selected crop through feature importance. The proposed method and its application on crop yield dataset is the first of its kind in literature.
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classifying land use change without high resolution imagery offers the potential to monitor vital aspects of global development agenda including climate smart agriculture, drought resistant crops, and sustainable land management. Utilizing a combination of capsule layers and long-short term memory layers with distributed attention, the present paper achieves state-of-the-art accuracy on temporal crop type classification at a 30x30m resolution with Sentinel 2 imagery.
The $K$-means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a $K$-means type algorithm that assigns observations to groups while estimating their skewness-transformation parameters. The resulting groups and transformation reveal general-structured clusters that can be explained by inverting the estimated transformation. Further, a modification of the jump statistic chooses the number of groups. Our algorithm is evaluated on simulated and real-life datasets and then applied to a long-standing astronomical dispute regarding the distinct kinds of gamma ray bursts.
Irish agtech company Cainthus uses vision technology to improve dairy herd management. Ireland's multi-generations of dairy farmers know a thing or two about raising dairy cows. Its more than 18,000 dairy farmers tend 1.4 million animals and are recognized globally for productivity and quality. So, it's no surprise that an Irish agtech company called Cainthus would invent a way to use artificial intelligence--the same technology developed for terrorist detection of humans--to manage dairy cows. At its simplest, Cainthus' technology has been described as facial recognition for cows, but Cainthus CEO Aidan Connolly explains that it is actually much more.
Businesses and nonprofits are finding novel ways to employ artificial intelligence in the developing world, using the tools to improve agriculture yields, infant health care, and entrepreneur earnings, according to speakers at MIT Technology Review's EmTech Digital conference in San Francisco on Tuesday. Solomon Assefa, who oversees IBM's research labs in Kenya and South Africa, said the company has been using AI to more accurately predict crop yields in specific regions, based on shifting weather patterns, soil moisture, and other conditions. This insight into growing conditions has helped local farmers raise financing to expand their operations, or make better decisions about the right seeds, appropriate fertilizer, and ideal times to plant and harvest. Separately, the tech giant's research lab has partnered with a startup, Hello Tractor, that links farmers in need of tractors with owners looking to lease equipment. By forecasting demand for the vehicles, IBM has also helped owners raise money to expand their fleet, boosting their profits, Assefa said.
In this paper, we propose a novel approach to tackle the multiple instance regression (MIR) problem. This problem arises when the data is a collection of bags, where each bag is made of multiple instances corresponding to the same unique real-valued label. Our goal is to train a regression model which maps the instances of an unseen bag to its unique label. This MIR setting is common to remote sensing applications where there is high variability in the measurements and low geographical variability in the quantity being estimated. Our approach, in contrast to most competing methods, does not make the assumption that there exists a prime instance responsible for the label in each bag. Instead, we treat each bag as a set (i.e, an unordered sequence) of instances and learn to map each bag to its unique label by using all the instances in each bag. This is done by implementing an order-invariant operation characterized by a particular type of attention mechanism. This method is very flexible as it does not require domain knowledge nor does it make any assumptions about the distribution of the instances within each bag. We test our algorithm on five real world datasets and outperform previous state-of-the-art on three of the datasets. In addition, we augment our feature space by adding the moments of each feature for each bag, as extra features, and show that while the first moments lead to higher accuracy, there is a diminishing return.