


Artificial intelligence enters the green plant world, and the smart courtyard market begins to take shape
Text/Stephen Wunker
It has been two decades since the use of smart homes in the consumer market to provide protection for home maintenance and home security through a combination of temperature, water and motion sensors. Although smart yards are an emerging concept, startup ePlant believes they understand where this trend is headed.
According to ePlant CEO Graham Hine, “Not only do people love trees, yards have a staggering impact on property values. According to a report from the National Association of Realtors , it can be said that landscape design has an impact of up to 30% on the sale price of a house. Improving the health of landscape trees has both emotional and financial impacts.
Previously, proper tree maintenance programs were complicated to operate and the results were unsatisfactory. Learning about bad tree behavior is time-consuming and error-prone, but homeowners can work hard and learn. Because symptoms of tree diseases are limited, entering tree photos into a computer system can result in numerous false indicators being generated. Not for the faint of heart, as detailed measurements require the use of a patchwork of sensors, batteries, solar panels, radios and antenna wiring.
new plan
ePlant is confident that it has found a more elegant solution, thanks to the rapid advancement of artificial intelligence. The system Hayne mentioned is set up to install a 3.2mm diameter screw in the tree and connect it to the sensor. The sensor can detect micron-scale changes in tree trunk diameter. As trees transpire water as their primary metabolic process, the trunk's diameter changes throughout the day within a range approximately equal to the width of a human hair. Like the human pulse, this tells us a lot. When combined with weather and irrigation data, we can study how trees respond to these events. ”
TreeTag engages in conversations with homeowners about the health of their yard through an app. Image source: EPLANT
The data is transmitted over a long-range wireless network to an Internet-connected gateway, and ePlant’s artificial intelligence system can then correlate the tree’s readings with atmospheric data, the reactions of other trees, and more to create a complex model of stimuli and responses. Predicting the behavior of healthy and damaged trees is how models are optimized through machine learning, which is exactly how artificial intelligence algorithms work.
is also generative AI
Not only that, ePlant combines the output of this algorithmic AI with a generative AI conversation engine to present the results in the form of interesting text, allowing "trees" to have conversations with their owners about their conditions and needs. The tree can be discussed with respect to its response to moisture, risk of insect damage, etc., when asked.
Why do this? "People tend to use an app once and forget about it," Hayne said. "We believe turning the process into a fun, gamified experience is key to ensuring it becomes a successful consumer product."
New application areas
While other companies in the industry focus on other aspects of smart yards, such as lighting and sprinkler systems, Hain focuses on other applications such as agricultural uses. For example, he says a vineyard doesn't want to overwater so that the grapes die, but you do want to get the right amount of sweetness. You also don’t want to underwater, as this will reduce fruit production. So getting the balance right is part of precision farming. ”
ePlant is researching the use of sensors to assess aspects such as carbon sequestration, forest health and disease spread. Although satellite data is currently the primary way to track these factors, sensor-level information provides a broad set of variables that can be evaluated.
The above is the detailed content of Artificial intelligence enters the green plant world, and the smart courtyard market begins to take shape. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
