Machine learning analysis of data gathered by acoustic recording devices is a promising new tool for monitoring the marbled murrelet and other secretive, hard-to-study species, research by Oregon State University and the U.S. Forest Service has shown. The threatened marbled murrelet is an iconic Pacific Northwest seabird that's closely related to puffins and murres, but unlike those birds, murrelets raise their young as far as 60 miles inland in mature and old-growth forests. "There are very few species like it," said co-author Matt Betts of the OSU College of Forestry. "And there's no other bird that feeds in the ocean and travels such long distances to inland nest sites. This behavior is super unusual and it makes studying this bird really challenging."

Vision-based segmentation in forested environments is a key functionality for autonomous forestry operations such as tree felling and forwarding. Deep learning algorithms demonstrate promising results to perform visual tasks such as object detection. However, the supervised learning process of these algorithms requires annotations from a large diversity of images. In this work, we propose to use simulated forest environments to automatically generate 43 k realistic synthetic images with pixel-level annotations, and use it to train deep learning algorithms for tree detection. This allows us to address the following questions: i) what kind of performance should we expect from deep learning in harsh synthetic forest environments, ii) which annotations are the most important for training, and iii) what modality should be used between RGB and depth. We also report the promising transfer learning capability of features learned on our synthetic dataset by directly predicting bounding box, segmentation masks and keypoints on real images. Code available on GitHub (https://github.com/norlab-ulaval/PercepTreeV1).

But they are a few problems relating to using a traditional R CNN. Mainly that processing an image takes a lot of time. As the model is extracting 2000 regions to check. Prediction with the model takes around 40 seconds. A better version of R CNN was created by the same person.

You might have heard about random forests as a somewhat-new analytical method in forestry. But how do they work?

A drone has been likened to a grasshopper because of its unique ability to leap into the air using its specially designed legs. The cargo-carrying automated vehicle is equipped with legs that let it jump 6.5 feet (two metres) into the air, taking off almost vertically. The craft, dubbed the Sparrow, costs £30,000 ($40,000) and can fly up to 62 miles (100km) at a speed of 112 mph (180kph). Delivery firms are pioneering a host of new technologies to tackle the last mile of deliveries. It is hoped the vehicles can cut the inefficiencies, and hence costs, of the final stage of delivery, in which packages are taken from a central hub to your door.

Forest loss is being driven by various factors, including commodity production, forestry, agriculture, wildfire, and urbanization. Curtis et al. used high-resolution Google Earth imagery to map and classify global forest loss since 2001. Just over a quarter of global forest loss is due to deforestation through permanent land use change for the production of commodities, including beef, soy, palm oil, and wood fiber. Despite regional differences and efforts by governments, conservationists, and corporations to stem the losses, the overall rate of commodity-driven deforestation has not declined since 2001. Global maps of forest loss depict the scale and magnitude of forest disturbance, yet companies, governments, and nongovernmental organizations need to distinguish permanent conversion (i.e., deforestation) from temporary loss from forestry or wildfire.

Most livestock and field crops rely heavily on the weather for their comfort, and providing water and energy. But China's more than 1.3 billion residents, a growing number of whom are becoming mid-income earners, are building up such an appetite that farmers are having to change the way they grow and sell food. In order to transform an ancient business that was largely run using intuition, the modern answer is technology. Artificial intelligence has come to the farmyard, helping to ensure the country's increasing numbers of pigs remain active and crop yields grow ever larger. This is the case for Wang Degen and his company Tequ Group, a major hog farm in Southwest China's Sichuan province.

Artificial Intelligence (AI) is now being hyped in earnest. The media is full of stories about its achievements: magical algorithms beating humans on their own turf in Jeopardy, the Chinese go-game and Texas hold' m, not to mention the stock traders and radiologists soon to be displaced by machines. IBM was very clever to name its AI software suite Watson. A bit of mystique is always intriguing. And that is not all: venture capital is pouring into AI start-ups – 15 billion US dollars since 2012!