Aug 7

Robot uses machine learning to harvest lettuce

0 comments

A vegetable-picking robot that uses machine learning to identify and harvest a commonplace, but challenging, agricultural crop has been developed by engineers.

 

 

The ‘Vegebot’, developed by a team at the University of Cambridge, was initially trained to recognise and harvest iceberg lettuce in a lab setting. It has now been successfully tested in a variety of field conditions in cooperation with G’s Growers, a local fruit and vegetable co-operative.

Although the prototype is nowhere near as fast or efficient as a human worker, it demonstrates how the use of robotics in agriculture might be expanded, even for crops like iceberg lettuce which are particularly challenging to harvest mechanically. The results are published in The Journal of Field Robotics.

Crops such as potatoes and wheat have been harvested mechanically at scale for decades, but many other crops have to date resisted automation. Iceberg lettuce is one such crop. Although it is the most common type of lettuce grown in the UK, iceberg is easily damaged and grows relatively flat to the ground, presenting a challenge for robotic harvesters.

“Every field is different, every lettuce is different,” said co-author Simon Birrell from Cambridge’s Department of Engineering. “But if we can make a robotic harvester work with iceberg lettuce, we could also make it work with many other crops.”

“At the moment, harvesting is the only part of the lettuce life cycle that is done manually, and it’s very physically demanding,” said co-author Julia Cai, who worked on the computer vision components of the Vegebot while she was an undergraduate student in the lab of Dr Fumiya Iida.

The Vegebot first identifies the ‘target’ crop within its field of vision, then determines whether a particular lettuce is healthy and ready to be harvested, and finally cuts the lettuce from the rest of the plant without crushing it so that it is ‘supermarket ready’. “For a human, the entire process takes a couple of seconds, but it’s a really challenging problem for a robot,” said co-author Josie Hughes.

The Vegebot has two main components: a computer vision system and a cutting system. The overhead camera on the Vegebot takes an image of the lettuce field and first identifies all the lettuces in the image, and then for each lettuce, classifies whether it should be harvested or not. A lettuce might be rejected because it’s not yet mature, or it might have a disease that could spread to other lettuces in the harvest.

The researchers developed and trained a machine learning algorithm on example images of lettuces. Once the Vegebot could recognise healthy lettuces in the lab, it was then trained in the field, in a variety of weather conditions, on thousands of real lettuces.

A second camera on the Vegebot is positioned near the cutting blade and helps ensure a smooth cut. The researchers were also able to adjust the pressure in the robot’s gripping arm so that it held the lettuce firmly enough not to drop it, but not so firm as to crush it. The force of the grip can be adjusted for other crops.

“We wanted to develop approaches that weren’t necessarily specific to iceberg lettuce so that they can be used for other types of above-ground crops,” said Iida, who leads the team behind the research.

In future, robotic harvesters could help address problems with labour shortages in agriculture, and could also help reduce food waste. At the moment, each field is typically harvested once, and any unripe vegetables or fruits are discarded. However, a robotic harvester could be trained to pick only ripe vegetables, and since it could harvest around the clock, it could perform multiple passes on the same field, returning at a later date to harvest the vegetables that were unripe during previous passes.

“We’re also collecting lots of data about lettuce, which could be used to improve efficiency, such as which fields have the highest yields,” said Hughes. “We’ve still got to speed our Vegebot up to the point where it could compete with a human, but we think robots have lots of potential in agri-tech.”

Iida’s group at Cambridge is also part of the world’s first Centre for Doctoral Training (CDT) in agri-food robotics. In collaboration with researchers at the University of Lincoln and the University of East Anglia, the Cambridge researchers will train the next generation of specialists in robotics and autonomous systems for application in the agri-tech sector. The Engineering and Physical Sciences Research Council (EPSRC) has awarded £6.6m for the new CDT, which will support at least 50 PhD students.

Reference: Simon Birrell et al. ‘A Field Tested Robotic Harvesting System for Iceberg Lettuce.’ Journal of Field Robotics (2019). DOI: 10.1002/rob.21888

New Posts
  • An updated analysis from OpenAI shows how dramatically the need for computational resources has increased to reach each new AI breakthrough. In 2018, OpenAI found that the amount of computational power used to train the largest AI models had doubled every 3.4 months since 2012. The San Francisco-based for-profit AI research lab has now added new data to its analysis. This shows how the post-2012 doubling compares to the historic doubling time since the beginning of the field. From 1959 to 2012, the amount of power required doubled every 2 years, following Moore’s Law. This means the doubling time today is more than seven times the previous rate. This dramatic increase in the resources needed underscores just how costly the field’s achievements have become. Keep in mind, the above graph shows a log scale. On a linear scale (below), you can more clearly see how compute usage has increased by 300,000-fold in the last seven years. The chart also notably does not include some of the most recent breakthroughs, including Google’s large-scale language model BERT, OpenAI’s large-scale language model GPT-2,  or DeepMind’s StarCraft II-playing model AlphaStar. In the past year, more and more researchers have sounded the alarm on the exploding costs of deep learning. In June, an analysis from researchers at the University of Massachusetts, Amherst, showed how these increasing computational costs directly translate into carbon emissions. In their paper, they also noted how the trend exacerbates the privatization of AI research because it undermines the ability for academic labs to compete with much more resource-rich private ones. In response to this growing concern, several industry groups have made recommendations. The Allen Institute for Artificial Intelligence, a nonprofit research firm in Seattle, has proposed that researchers always publish the financial and computational costs of training their models along with their performance results, for example. In its own blog, OpenAI suggested policymakers increase funding to academic researchers to bridge the resource gap between academic and industry labs
  • StarckGate is happy to work together with Asimov that will be aiming to radically advance humanity's ability to design living systems. They strive to enable biotechnologies with global benefit by combining synthetic biology and computer science. With their help we will able to grasp the following domains better Synthetic Biology Nature has evolved billions of useful molecular nanotechnology devices in the form of genes, across the tree of life. We catalog, refine, and remix these genetic components to engineer new biological systems. Computational Modeling Biology is complex, and genetic engineering unlocks an unbounded design space. Computational tools are critical to design and model complex biophysical systems and move synthetic biology beyond traditional brute force screening. Cellular Measurement Genome-scale, multi-omics measurement technologies provide deep views into the cell. These techniques permit pathway analysis at the scale of a whole cell, and inspection down at single-nucleotide resolution. Machine Learning We are developing machine learning algorithms that bridge large-scale datasets with mechanistic models of biology. Artificial intelligence can augment human capabilities to design and understand biological complexity.
  • The use of AI (artificial intelligence) in agriculture is not new and has been around for some time with technology spans a wide range of abilities—from that which discriminates between crop seedlings and weeds to greenhouse automation. Indeed, it is easy to think that this is new technology given the way that our culture has distanced so many facets of food production, keeping it far away from urban spaces and our everyday reality. Yet, as our planet reaps the negative repercussions of technological and industrial growth, we must wonder if there are ways that our collective cultures might be able to embrace AI’s use in food production which might include a social response to climate change. Similarly, we might consider if new technology might also be used to educate future generations as to the importance of responsible food production and consumption. While we know that AI can be a force for positive change where, for instance, failures in food growth can be detected and where crops can be analyzed in terms of disease, pests and soil health, we must wonder why food growth has been so divorced from our culture and social reality. In recent years, there has been great pushback within satellite communities and the many creations of villages focussed upon holistic methods of food production. Indeed, RegenVillages is one of many examples where vertical farming, aquaponics, aeroponics and permaculture are part of this community's everyday functioning. Moreover, across the UK are many ecovillages and communities seeking to bring back food production to the core of social life. Lammas is one such ecovillage which I visited seven years ago in Wales which has, as its core concept, the notion of a “collective of eco-smallholdings working together to create and sustain a culture of land-based self-reliance.” And there are thousands of such villagesacross the planet whereby communities are invested in working to reduce their carbon footprint while taking back control of their food production. Even Planet Impact’s reforestation programs are interesting because the links between healthy forests and food production are well known as are the benefits of forest gardening which is widely considered a quite resilient agroecosystem. COO & Founder of Planetimpact.com, Oscar Dalvit, reports that his company’s programs are designed to educate as much as to innovate: “With knowledge, we can fight climate change. Within the for-profit sector, we can win this battle.” Forest gardening is a concept that is not only part of the permaculture practice but is also an ancient tradition still alive and well in places like Kerala, India and Martin Crawford’s forest garden in southwest England where his Agroforestry Research Trust offers courses and serves as a model for such communities across the UK. But how can AI help to make sustainable and local farming practices over and above industrial agriculture? Indeed, one must wonder if it is possible for local communities to take control of their food production. So, how can AI and other new tech interfaces bring together communities and food production methods that might provide a sustainable hybrid model of traditional methods and innovative technology? We know already that the IoT (internet of things) is fast becoming that virtual space where AI is being implemented to include within the latest farming technology. And where businesses invested in robotics are likewise finding that there is no ethical implementation of food technology, we must be mindful of how strategies are implemented which incorporate the best of new tech with the best of old tech. Where AI is helping smaller farms to become more profitable, all sorts of digital interfaces are transmitting knowledge, education and the expansion of local farming methods. This means, for instance, that garden maintenance is continued by others within the community as some members are absent for reasons of vacation or illness. Together with AI, customer experience is as much a business model as it is a local community standard for communication and empowerment. The reality is that industrial farming need not take over local food production and there are myriad ways that communities can directly respond to climate change and the encroachment of big agriculture. The health benefits of local farming practices are already well known as are the many ways that smartphone technology can create high-yield farms within small urban spaces. It is high time that communities reclaim their space within urban centers and that urban dwellers consider their food purchasing and consumption habits while building future sustainability which allows everyone to participate in local food production. As media has recently focussed upon AI and industrial farming, we need to encourage that such technology is used to implement local solutionsthat are far more sustainable and realistic instead of pushing big agriculture.

Proudly created by Starckgate 

© 2020 by Starckgate

  • White Facebook Icon
  • White Twitter Icon
  • White Instagram Icon