Experts say robotics and artificial intelligence (AI) will drive a deep and transformative change in the agricultural world during the coming decades. It is already clear that 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 mechanised many agricultural tasks, or just as advances in seed and agrochemical technology removed the human from many activities.
Fpcfreshtalkdaily.co.uk reports on new data from research company IDTechEX, showing that the upcoming changes are already a question of when and not if. The transformation will not be overnight, but nonetheless, robotics and AI are 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.
The report analyses all the emerging product types, including autonomous robots taking plant-specific precision action, intelligent vision-enabled robotic implements, diverse robotic fresh fruit harvesters, highly automated and autonomous tractors and high-power farm vehicles, drones, automatic milking, and so on. It provides interview-based company profiles and analysis of all of the key companies and innovators.
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, such as water-stress and disease.
In fruit picking, machine vision technology can identify and localise different visible fruits against complex and varying backgrounds with a high success rate. The rise of deep learning-based image recognition technologies has caused a leap in performance, sending everybody back to the drawing board, including the older start-ups and some who had given up. This technology improves algorithm precision, lowering the false positives which waste time. Crucially, a clear pathway exists for algorithm development for new fruit- environment combinations, enabling the applicability of machine detection and localisation to be extended to many fruits.
Click here for more information on IDTechEx report "Agricultural Robots, Drones, and AI: 2020-2040: Technologies, Markets, and Players."