Construction sites frequently require removing large rocks before excavation or grading can proceed. Human operators typically extract these boulders using only standard digging buckets, avoiding time-consuming tool changes to specialized grippers. This task demands manipulating irregular objects with unknown geometries in harsh outdoor environments where dust, variable lighting, and occlusions hinder perception. The excavator must adapt to varying soil resistance--dragging along hard-packed surfaces or penetrating soft ground--while coordinating multiple hydraulic joints to secure rocks using a shovel. Current autonomous excavation focuses on continuous media (soil, gravel) or uses specialized grippers with detailed geometric planning for discrete objects. These approaches either cannot handle large irregular rocks or require impractical tool changes that interrupt workflow. We train a reinforcement learning policy in simulation using rigid-body dynamics and analytical soil models. The policy processes sparse LiDAR points (just 20 per rock) from vision-based segmentation and proprioceptive feedback to control standard excavator buckets. The learned agent discovers different strategies based on soil resistance: dragging along the surface in hard soil and penetrating directly in soft conditions. Field tests on a 12-ton excavator achieved 70% success across varied rocks (0.4-0.7m) and soil types, compared to 83% for human operators. This demonstrates that standard construction equipment can learn complex manipulation despite sparse perception and challenging outdoor conditions.

Changing climatic conditions, the shortage of skilled workers, the use of pesticides--a wide range of factors have an impact on the quality and flow of agricultural processes. Researchers at the Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, HHI are aiming to make this more efficient and sustainable by means of cloud and AI technologies. As part of the "NaLamKI" project, they are working with partners to establish a software-as-a-service platform that collects device and machine data to form a data basis for forecasts and decision-making aids. The agricultural sector is facing major challenges: German farmers are already feeling the far-reaching effects of climate change and will have to adapt to this to a greater extent in the future. Rising temperatures and changes in precipitation affect all agricultural variables, ranging from crop growth to crop rotations right through to tillage.

Technology is reshaping most activities humans do today. Concepts like Smart Farming have gained prominence as newer methods for crop and farm management are on the rise. It is making farming an efficient and profitable activity. Going by the estimates, there will be a 15% increase in the demand for agricultural products in the coming decade. Using tech solutions to cope up is an ideal way forward.

The industry as a whole is facing huge challenges, from rising costs of supplies, a shortage of labor, and changes in consumer preferences for transparency and sustainability. There is increasing recognition from agriculture corporations that solutions are needed for these challenges. In the last 10 years, agriculture technology has seen a huge growth in investment, with $6.7 billion invested in the last 5 years and $1.9 billion in the last year alone. Major technology innovations in the space have focused around areas such as indoor vertical farming, automation and robotics, livestock technology, modern greenhouse practices, precision agriculture and artificial intelligence, and blockchain. Farm automation, often associated with "smart farming", is technology that makes farms more efficient and automates the crop or livestock production cycle.

Farmers can analyze a variety of things with thousands of data points collected from their farms about the climate, temperature, and soil conditions. This can help them decide which types of seeds to use considering the soil conditions at the time. Farmers can precisely target the weeds with AI sensors and can apply the right amount of herbicides needed to treat the most diseased crops. This improves the overall quality of their crop. With substantial amounts of data now available, farmers are able to create seasonal models that highly predict agricultural accuracy and productivity.

During a workshop hosted at the International Conference on Learning Representations (ICLR) 2020, taking place on the web this week, panelists discussed how AI and machine learning might be -- and already has been -- applied to agricultural challenges. As several experts pointed out, countries around the world face a food supply shortfall -- an estimated 9% of the population (697 million people) are severely "food insecure," meaning they're without reliable access to affordable, nutritious food. Factors like labor shortages, the spread of pests and pathogens, and climate change threaten to escalate the crisis. IBM scientists spoke about their work in Africa with agricultural "digital twins," or digital models of crops used to forecast specific crop yields. And a team from the University of California, Davis detailed an effort to use satellite images to predict foraging conditions for livestock in Kenya.

Cities around the world are getting smarter. Already, street lights in places like San Diego are turning off, and conserving energy, when vehicles and pedestrians aren't around. Soon, connected garbage cans will tell waste haulers when they need to be emptied, optimizing collection routes. Smart buildings will notify maintenance staff of impending repair needs. And parking spots will find you, instead of the other way around.

CHICAGO – Agribusiness is increasingly turning to natural and sustainable alternatives to chemicals as consumers rebuff genetically modified foods and concerns grow over Big Ag's role in climate change. At the heart of the trend are innovations that harness beneficial microorganizms in the soil, including seed-coatings of naturally occurring bacteria and fungi that can do the same work as traditional chemicals, from warding off pests to helping plants flourish, according to a global patent study by research firm GreyB Services. Much of the research in crop biotech is centered in the United States, China, Germany, Japan and South Korea, according to the U.N. agency WIPO. "Both entrepreneurs and investors are saying, 'Hey, the writing is on the wall, we're entering a post-chemical world,'" said Rob LeClerc, chief executive officer of AgFunder, an online venture-capital platform. "The seed companies who have billions in market cap are like'We need to do something,' and everyone recognizes the opportunity."

Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully-connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN have three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.

Perhaps the coolest thing about IBM's 9th "Five Innovations that will Help Change our Lives within Five Years" predictions is that none of them sound like science fiction. "With advances in artificial intelligence and nanotechnology, we aim to invent a new generation of scientific instruments that will make the complex invisible systems in our world today visible over the next five years," said Dario Gil, vice president of science & solutions at IBM Research in a statement. Among the five areas IBM sees as being key in the next five years include artificial intelligence, hyperimaging and small sensors. In five years, what we say and write will be used as indicators of our mental health and physical wellbeing. Patterns in our speech and writing analyzed by new cognitive systems will provide tell-tale signs of early-stage mental and neurological diseases that can help doctors and patients better predict, monitor and track these diseases.