Climate control is crucial for greenhouse production as it directly affects crop growth and resource use. Reinforcement learning (RL) has received increasing attention in this field, but still faces challenges, including limited training efficiency and high reliance on initial learning conditions. Interactive RL, which combines human (grower) input with the RL agent's learning, offers a potential solution to overcome these challenges. However, interactive RL has not yet been applied to greenhouse climate control and may face challenges related to imperfect inputs. Therefore, this paper aims to explore the possibility and performance of applying interactive RL with imperfect inputs into greenhouse climate control, by: (1) developing three representative interactive RL algorithms tailored for greenhouse climate control (reward shaping, policy shaping and control sharing); (2) analyzing how input characteristics are often contradicting, and how the trade-offs between them make grower's inputs difficult to perfect; (3) proposing a neural network-based approach to enhance the robustness of interactive RL agents under limited input availability; (4) conducting a comprehensive evaluation of the three interactive RL algorithms with imperfect inputs in a simulated greenhouse environment. The demonstration shows that interactive RL incorporating imperfect grower inputs has the potential to improve the performance of the RL agent. RL algorithms that influence action selection, such as policy shaping and control sharing, perform better when dealing with imperfect inputs, achieving 8.4% and 6.8% improvement in profit, respectively. In contrast, reward shaping, an algorithm that manipulates the reward function, is sensitive to imperfect inputs and leads to a 9.4% decrease in profit. This highlights the importance of selecting an appropriate mechanism when incorporating imperfect inputs.

In a windowless shack on the far outskirts of Fresno, an ominious red glow illuminates a lab filled with X-ray machines, shelves of glowing boxes, a quietly humming incubator and a miniature wind tunnel. While the scene looks like something straight out of a sci-fi movie, its actually part of an experimental program to prevent a damaging almond pest from successfully mating. With California almond growers reeling from dropping nut prices and rising costs, the pests have only added to their woes. Every year, the navel orangeworm eats through roughly 2% of California's almonds before they can make it to grocery store shelves. Last year, it was almost double that.

The Canadian fertilizer company Nutrien, for example, operates two dozen manufacturing and processing facilities spread across the globe and nearly 2,000 retail stores in the Americas and Australia. To collect underutilized data from its industrial operations, and gain greater visibility into its supply chain, the company relies on a combination of cloud technology and artificial intelligence/machine learning (AI/ML) capabilities. "A digital supply chain connects us from grower to manufacturer, providing visibility throughout the value chain," says Adam Lorenz, senior director for strategic fleet and indirect procurement at Nutrien. This visibility is critical when it comes to navigating the company's supply chain challenges, which include seasonal demands, weather dependencies, manufacturing capabilities, and product availability. The company requires real-time visibility into its fleets, for example, to identify the location of assets, see where products are moving, and determine inventory requirements.

Dormant pruning, or the removal of unproductive portions of a tree while a tree is not actively growing, is an important orchard task to help maintain yield, requiring years to build expertise. Because of long training periods and an increasing labor shortage in agricultural jobs, pruning could benefit from robotic automation. However, to program robots to prune branches, we first need to understand how pruning decisions are made, and what variables in the environment (e.g., branch size and thickness) we need to capture. Working directly with three pruning stakeholders -- horticulturists, growers, and pruners -- we find that each group of human experts approaches pruning decision-making differently. To capture this knowledge, we present three studies and two extracted pruning protocols from field work conducted in Prosser, Washington in January 2022 and 2023. We interviewed six stakeholders (two in each group) and observed pruning across three cultivars -- Bing Cherries, Envy Apples, and Jazz Apples -- and two tree architectures -- Upright Fruiting Offshoot and V-Trellis. Leveraging participant interviews and video data, this analysis uses grounded coding to extract pruning terminology, discover horticultural contexts that influence pruning decisions, and find implementable pruning heuristics for autonomous systems. The results include a validated terminology set, which we offer for use by both pruning stakeholders and roboticists, to communicate general pruning concepts and heuristics. The results also highlight seven pruning heuristics utilizing this terminology set that would be relevant for use by future autonomous robot pruning systems, and characterize three discovered horticultural contexts (i.e., environmental management, crop-load management, and replacement wood) across all three cultivars.

South Africa's Aerobotics is utilizing artificial intelligence (AI) in helping fruit and nut farmers in over 18 countries. JOHANNESBURG - South Africa's Aerobotics is utilizing artificial intelligence (AI) in helping fruit and nut farmers improve crop yields. Although the Cape Town-based company only started nine years ago, it is already operating in 18 countries, with the U.S. being their largest market, followed by South Africa, Australia, Spain and Portugal. Its customers produce tens of millions of tons of fresh produce every year. California is now ground zero for Aerobotics – where the company has the biggest concentration of customers.

Charles Valentine Riley of Missouri played a key role in saving the French wine industry -- check out his amazing life story. Champagne may be in short supply rather soon, according to ClimateAI, a climate resilience platform based in San Francisco. The company said the changing global climate could threaten the popular celebratory beverage. ClimateAI's artificial intelligence-driven data has suggested that hundreds of grape varieties could be on the brink of extinction, including champagne-making grapes like pinot noir, chardonnay and merlot. Will Kletter, ClimateAI's vice president of operations and strategy, told Fox News Digital in an interview that champagne and wine drinkers may be at a loss by the year 2050.

Cutting-edge NASA imaging technology can detect early signs of a plant virus that, if unaddressed, often proves devastating for wineries and grape growers, new research has found. While the breakthrough is good news for the wine and grape industry, which loses billions of dollars a year to the crop-ruining disease, it could eventually help global agriculture as a whole. Using intricate infrared images captured by airplane over California's Central Valley, researchers were able to distinguish Cabernet Sauvignon grape vines that were infected but not showing symptoms -- before the point at which growers can spot the disease and respond. The technology, coupled with machine learning and on-the-ground analysis, successfully identified infected plants with almost 90% accuracy in some cases, according to two new research papers. "This is the first time we've ever shown the ability to do viral disease detection on the airborne scale," said Katie Gold, an assistant professor of grape pathology at Cornell University and a lead researcher on the project.

While Silicon Valley has transformed every industry from health care to banking, agriculture has remained largely untouched -- until now. Ever since OpenAI's breakthrough with ChatGPT, the term AI has been thrown around so many times it's starting to lose its meaning. Nevertheless, artificial intelligence has seeped into every industry from enterprise software to autonomous vehicles, taking around 10% of global venture dollars in 2022. Grow your revenue with all-in-one prospecting solutions powered by the leader in private-company data. Agriculture has not been immune to the AI revolution that has gripped the tech world.

University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) researchers plan to use artificial intelligence (AI) technology to quantify damage to fruits and vegetables caused by extreme weather events. One such extreme weather event was Hurricane Ian, which struck Florida on Sept. 28, 2022. The storm's damage to all crops, livestock and aquaculture products was initially estimated at up to $1.56 billion, based on farmer surveys. This figure is critical because growers need to know the extent of crop loss to file insurance claims and apply for other recovery aid. Ampatzidis has already developed Agroview and AgroSense at his lab at the Southwest Florida Research and Education Center.

The term "artificial intelligence" has generated pages of dystopian copy surrounding the displacement of jobs and the dehumanization of the workplace, but in farm fields, AI and machine learning are proving to be an efficient ally of growers for combating weeds and keeping expenses in check. In the 1980s, researchers were elated when they developed sprayers capable of on-the-go determination between bare ground and growing plants -- a breakthrough that paved the way for what is now widely known as GreenSeeker technology. Crude sensors that differentiated the color of soil vs. the color of green plant material led to precision spectral radiance technology that provides the backbone of today's remote sensing used in precise fertilizer placement. As digital memory became increasingly miniaturized, it was possible to photograph and catalog various weeds in computer files used by applicators to further differentiate weeds from growing crops as they travel across fields -- the entry of AI into agriculture, a debut that will forever change farm practices. John Deere's first See & Spray system introduced in 2021 allowed growers to reduce their non-residual pre-emergence herbicide use by more than 75% by targeting and spraying only weeds on fallow ground.