This chapter critiques the dominant reductionist approach in AI and work studies, which isolates tasks and skills as replaceable components. Instead, it advocates for a systemic perspective that emphasizes the interdependence of tasks, roles, and workplace contexts. Two complementary approaches are proposed: an ethnographic, context-rich method that highlights how AI reconfigures work environments and expertise; and a relational task-based analysis that bridges micro-level work descriptions with macro-level labor trends. The authors argue that effective AI impact assessments must go beyond predicting automation rates to include ethical, well-being, and expertise-related questions. Drawing on empirical case studies, they demonstrate how AI reshapes human-technology relations, professional roles, and tacit knowledge practices. The chapter concludes by calling for a human-centric, holistic framework that guides organizational and policy decisions, balancing technological possibilities with social desirability and sustainability of work.

Artificial Intelligence (AI) has emerged as a key driver of precision agriculture, facilitating enhanced crop productivity, optimized resource use, farm sustainability, and informed decision-making. Also, the expansion of genome sequencing technology has greatly increased crop genomic resources, deepening our understanding of genetic variation and enhancing desirable crop traits to optimize performance in various environments. There is increasing interest in using machine learning (ML) and deep learning (DL) algorithms for genotype-to-phenotype prediction due to their excellence in capturing complex interactions within large, high-dimensional datasets. In this work, we propose a new LSTM autoencoder-based model for barley genotype-to-phenotype prediction, specifically for flowering time and grain yield estimation, which could potentially help optimize yields and management practices. Our model outperformed the other baseline methods, demonstrating its potential in handling complex high-dimensional agricultural datasets and enhancing crop phenotype prediction performance.

Researchers in the UK have successfully grown the world's first crop of barley using nothing but robot tractors and drones. Hands Free Hectare is an experimental farm run by researchers from Harper Adams University, United Kingdom. Its aim is to sow, grow and harvest crops of spring barley using only unmanned vehicles, automated control systems and open-source technology. The project offers a glimpse of what the future of agriculture may be like. "We have been able to show the public that this is something that isn't too far ahead in the future, and it could be happening now," explains Martin Abell, mechatronics research engineer.

Swarms of drones buzz overhead, while robotic vehicles crawl across the landscape. Orbiting satellites snap high-resolution images of the scene far below. Not one human being can be seen in the pre-dawn glow spreading across the land. This is a snapshot of the farm of the future. Every phase of the operation--from seed to harvest--may someday be automated, without the need to ever get one's fingernails dirty.

Driverless tractors, combine harvesters and drones have grown a field of crops in Shropshire in a move that could change the face of farming. The autonomous vehicles followed a pre-determined path set by GPS to perform each task, while the field was monitored by scientists using self-driving drones. The project, called hands Free Hectare, began with autonomous tractors drilling channels to precise depths for the barley seeds to be planted. The tractor was also used to plant seeds and spray fungicides, herbicides, and fertilisers. An automated combine harvester then harvested the field of barley.

During the Hands Free Hectare project, no human set foot on the field between planting and harvest--everything was done by robots. To make these decisions, robot scouts (including drones and ground robots) surveyed the field from time to time, sending back measurements and bringing back samples for humans to have a look at from the comfort of someplace warm and dry and clean. With fully autonomous farm vehicles, you can use a bunch of smaller ones much more effectively than a few larger ones, which is what the trend has been toward if you need a human sitting in the driver's seat. Robots are only going to get more affordable and efficient at this sort of thing, and our guess is that it won't be long before fully autonomous farming passes conventional farming methods in both overall output and sustainability.

A computationally simple genome-wide association study (GWAS) algorithm for estimating the main and epistatic effects of markers or single nucleotide polymorphisms (SNPs) is proposed. It is based on the intuitive assumption that changes of alleles corresponding to important SNPs in a pair of individuals lead to large difference of phenotype values of these individuals. The algorithm is based on considering pairs of individuals instead of SNPs or pairs of SNPs. The main advantage of the algorithm is that it weakly depends on the number of SNPs in a genotype matrix. It mainly depends on the number of individuals, which is typically very small in comparison with the number of SNPs. Numerical experiments with real data sets illustrate the proposed algorithm.

A team at Harper Adams University are trying to grow and then harvest a field of barley using only robots and drones. If they succeed it will be a world first and we're following their progress. Last time we watched as the team from Harper Adams started to prepare for this year long experiment. They were using a small robot, not much bigger than a toy car, to refine the steering system they wanted to use. But at that point they were still waiting on their tractor to be delivered and couldn't find a suitable combine harvester. Since then things have moved on pretty quickly with triumphs and also some sleepless nights.

This paper describes a system that automatically transforms a PDDL encoding, calls a planner to solve the transformed representation, and translates the solution back into the original representation. The approach involves counting objects that are indistinguishable, rather than treating them as individuals, which eliminates some unnecessary combinatorial explosion.