Application of artificial intelligence in agriculture
Agriculture is the foundation for human survival. It occupies a basic position in the three industries and is crucial to the stability and development of the economy and society. However, with the rapid growth of population, the gradual reduction of cultivated land area and the acceleration of urbanization, the challenges facing agriculture have become increasingly severe. In order to cope with this challenge, both domestic and foreign countries are exploring the use of information technology to improve the quality and efficiency of agriculture. Among them, the new model of smart agriculture based on artificial intelligence is developing rapidly. It’s no exaggeration to say that artificial intelligence is disrupting the agricultural sector. Agriculture as we know it is being redefined. Artificial intelligence in agriculture can be divided into five categories:
1, Agricultural robots
Many technicians are now developing autonomous robots and programming them to handle The emergence of automation for important agricultural tasks, such as harvesting crops with higher productivity and faster speed than humans, can help solve the problem of labor shortage.
2, Crop and soil monitoring
Artificial intelligence can help farmers find irrigation loopholes, optimize irrigation systems, and Measuring the effectiveness of crop irrigation methods. As the world's population grows and the effects of drought become greater, water conservation becomes increasingly important. Efficient water use can greatly impact a farm's bottom line and contribute to global efforts to conserve water. Columbus said linear artificial intelligence programs are used to calculate the optimal amount of water needed for a specific field or crop to achieve desired yield levels.
3. Intelligent planting
In traditional agriculture, a lot of manpower and material resources are required; and equipped with artificial intelligence technology will help ease the burden on farmers, such as It is said that through AI management of irrigation and water use, image-based nutrient and fertilizer usage solutions, and even through AI, it is possible to predict the correct harvest time of crops, which greatly reduces the demand for labor on the land.
4. Livestock Monitoring
The ability to conduct high-level monitoring of livestock gives producers an advantage over competitors who have not yet invested in AI-enhanced agricultural technology. Columbus said farmers can monitor food intake, activity levels and vital signs to better understand the optimal conditions for better milk or meat production. Real-time health observations also enable farmers to quickly distinguish livestock infected with infectious diseases from healthy livestock and quickly address injuries and unexpected livestock behavior.
5, Use of Drones
The drone market in agriculture is expected to reach US$480 million by 2027, and drones The use is designed to help users increase crop yields and reduce costs. The drone's route is first programmed, and once deployed the device will utilize computer vision to record images and upload the captured data, using algorithms to integrate and analyze the captured images and data to provide a detailed analysis report.
The deep integration of artificial intelligence and agriculture in my country still faces multiple challenges, for example, Rural network infrastructure is weak, agricultural technology is still in the basic stage, and the research and development of artificial intelligence agricultural robots is not yet very mature. It is inevitable that more or less problems will arise during the put into use, etc. This requires relevant departments to start from the basics. Facilities, technology supply, industrial demand and other aspects will be considered to comprehensively promote the in-depth integration of artificial intelligence and agriculture and explore effective paths for the high-quality development of modern agriculture. In terms of supporting capabilities, efforts will be made to strengthen the construction of rural network infrastructure and agricultural information service platforms; in terms of technology supply, we will continue to improve the supply level of artificial intelligence technology in the agricultural field; in terms of industrial demand, we will vigorously cultivate farmers' willingness and ability to apply artificial intelligence, and continue to Provide technical guidance and dissemination of relevant knowledge.
It is believed that in the near future, with the continuous development of artificial intelligence technology, its large-scale application in the agricultural field will eventually be realized. Artificial intelligence has a bright future. Looking forward to the future, my country's agriculture will enter a new era of intelligence.
The above is the detailed content of Application of artificial intelligence in agriculture. 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
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
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
1387
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
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
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
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
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
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
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
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
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
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
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
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
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
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
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
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
