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Agricultural Robots and AI: A Question of When rather than If

Agricultural Robots and AI: A Question of When rather than If


Robotics and artificial intelligence (AI) will drive a deep and transformative change in the agricultural world during the coming decades. Seeing, localising, and taking plant-specific intelligent action are no longer the exclusive realm of humans. Machines have demonstrated the technical viability and the emphasis has long shifted to the finer details of ROI, reliability, business model, etc. As such, a new class of activities in agriculture are prone to automation, just as advances in power and motion technologies mechanized many agricultural tasks, or just as advances in seed and agrochemical technology removed the human from many activities. 

In an article published by market research firm IDTechEx, which has studied the technologies, applications, products, and players in agricultural robotics and AI for the past five years, the firm argues that the upcoming changes are already a question of when and not if. The article is based on the IDTechEx report “Agricultural Robots, Drones, and AI: 2020-2040: Technologies, Markets, and Players”, which covers the latest developments and reflects our latest insights, analysis, and market projections.  

The transformation will not be overnight, but nonetheless, robotics and AI are an inevitability in the evolution of agricultural tools and practises. The scale of the potential is demonstrated in the chart below, which shows the forecasted long-term growth in annual unit sales (vs accumulated fleet size) of various autonomous and/or robotic solutions.

Agricultural Robots: A Cost-Effective Precision Revolution?

Machine vision technology is often a core competency of these robots, enabling the robots to see, identify, localise, and to take some intelligent site-specific action on individual plants. The machine vision increasingly uses deep learning algorithms often trained on expert-annotated image datasets, allowing the technology to far exceed the performance of conventional algorithms and to match or even exceed even that of expert agronomists. Crucially, this approach enables a long-term technology roadmap, which can be extended to recognise all types of crops and to analyse their associated conditions, e.g., water-stress, disease, etc.

Many versions of this emerging robotic class are autonomous.  The autonomy challenge is much simpler than a car. The environment is well controlled and predictable, and the speed of travel is low. The legislation is today a hindrance, including in places such as California, but will become more accommodative relatively soon.

The rise of autonomous robots, provided they require little remote supervision, can alter the economics of machine design, enabling the rise of smaller and slower machines. Indeed, this elimination of the driver overhead per vehicle is the basis of the swarm concept. There is clearly a large productivity gap today between current large and high-power vehicles and those composed of fleets of slow small robots. This productivity gap however can narrow as the latter has substantial room for improvement.

The first major target market is in weeding. The ROI benefits here are driven by labour savings, chemical savings, boosted yields, and less land compaction. Precision action (spraying, mechanical, or electrical) reduces consumption of agrochemicals by 90% compared to untargeted application.  It also improves yield (e.g., by 5-10%) because collateral damage of the crops by untargeted chemical application can be minimized. This technology can further enable farmers to tackle herbicide-resistant weeds, which are a growing problem, especially in some hotspots. Finally, the robots leave behind no unusable compacted soil.

These robots are evolving. Many robots have already grown in size and capability, offering faster speeds, higher frame-per-seconds, more ruggedised designs, higher on-board energy for longer operation time and a heavier load, and so on. This evolution will inevitably continue, just as it did with all other agricultural tools and vehicles. We are still at the beginning. The deployed fleet sizes worldwide are small, but this is about to change (see the chart above).


 TAGS: robots, AI, agriculture, IDTechex, research, technology

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France: higher consumption of bananas, berries, exotics

Though French consumption has been stable over the past 10 years, bananas and exotic products have grown by around 20% and berries by 30% while home-grown fruits have fallen by 15%

The stable volumes of fruit and vegetable purchases in the past 10 years mask large variations by species and a sharp rise in retail prices.

These are the main lessons to be learnt from the CTIFL report and Kantar Worldpanel data, which monitor the purchases of 12,000 representative French households.

In 2015, French households purchased an average of 166 kg of fruit and vegetables (excluding potatoes and fresh-cut) compared to 170 kg in 2005. By volume, more fruit are bought than vegetables (84 kg and 81 kg respectively).

While households are consuming slightly less fruit and vegetables on average than 10 years ago, household size has fallen over this period. In fact, the overall consumption of French households as a whole has grown by 6% during the 2005-2015 period.

Another trend is that fruit and vegetables are bought more frequently (63.6 acts of purchase in 2015 against 61 in 2005) but in smaller volumes (2.6 kg compared to 2.8 kg). “This phenomenon is explained by renewed interest in shopping locally and a certain disaffection with very large retail stores,” said Christian Hutin of Ctifl, author of the report on the panel data for Infos Ctif.

Exotic fruit have the wind in their sails

Over the past 10 years the price of fruit and vegetables has risen by 20%. The climb was moderate up to 2010 and has accelerated since then.

“The result is that household spending is higher for the same proportion of fruit and vegetables,” said Christian Hutin.

Prices have risen more for fruit (24%) than for vegetables (17%).

Kantar Worldpanel distinguishes three major fruit categories: mainland fruit (apples, pears, cherries, apricots, peaches/nectarines, plums, strawberries, kiwifruit and berries), citrus fruit (oranges, lemons, limes, clementinas/mandarins, grapefruit) and exotic fruit (bananas, pineapples, mangoes, avocados, litchis).

In volume terms, purchases of French mainland fruit have fallen considerably over the past 10 years (down by 13% to 41 kg per household in 2015), have remained stable for citrus fruit (25 kg per household in both 2015 and 2005) and have risen sharply for exotic fruit (up by 23% to 19 kg per household in 2015).

Within each category, however, volumes vary considerably for different products (see box). This is also the case for vegetables, with a downward trend for fresh vegetables and a 7% rise in fresh-cut vegetable purchase volumes.


This article appeared in edition 146 of Eurofresh Distribution magazine. Read more from that edition online here.