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How Machine Learning can Revolutionize the Agricultural Industry

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How Machine Learning can Revolutionize the Agricultural Industry

Machine learning has evolved over the last few years along with other big data technologies and advanced computing to transform industries all over the world, and the agricultural sector is no exception.

With its advancements, machine learning in agriculture has been able to address a number of issues that the industry has been encountering.

Businesses can achieve success only when they constantly outperform their rivals in decision-making, and the agricultural sector is no exception. Through machine learning in agriculture, farmers now have access to more advanced data and analytics tools, facilitating better decisions, increased productivity, and decreased waste in the production of food and fuels, all while reducing unfavorable environmental effects.

How is Machine Learning a Great Fit for Agriculture?

With the assistance of highly precise algorithms, the growing idea of “smart farming” boosts the efficacy and productivity of agriculture. Machine learning — a branch of science that allows machines to learn without being explicitly programmed — is the mechanism behind it. To open up new possibilities for unraveling, analyzing, and comprehending data-intensive processes in agricultural organizational settings, it has evolved in tandem with big data technologies and powerful computers. Farmers can now predict agricultural yield and evaluate crop quality, determine plant species, and diagnose plant diseases and weed infestations at seemingly unimaginable levels, using sensors in the farm in accordance with ML-enabled electronic innovations. Throughout the entire cycle of planting, growing, and harvesting, machine learning in agriculture is prominent. It starts with sowing a seed, goes through soil testing, seed breeding, and water supply measurement, and concludes with robots collecting the harvest and using computer vision to assess its degree of ripeness. The amount of data accessible to farmers today is overwhelming without the aid of machine learning technology. Lots of data can be promptly assessed by ML, with the help of which it recommends the most profitable strategy. For instance, it can advise on when to plant in order to ward off pests and diseases. The advantages of digital farming are legitimate; it may assist growers in making the best input decisions in order to enhance production and profit. Furthermore, it can assist farmers in determining actual expenses on a field-by-field basis rather than only on a farm-wide one.

Applications of Machine Learning in Agriculture

Machine learning has extensively grown in the agricultural sector in recent years. Following are its applications in the farming industry:

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Species Breeding and Recognition

The laborious process of species selection entails looking for particular genes that will guarantee efficient responsiveness to water and nutrients. Ideal plant species will be able to withstand climatic change, be disease-resistant, have more nutrients, and taste better.

For a thorough investigation of crop performance, machine learning enables us to extract information from decades of field data. This data is used to create a probability model that predicts which traits will give a plant a desirable genetic advantage.

Species identification in crops has typically been carried out using straightforward comparisons, such as the color and shape of the leaves. Utilizing more advanced approaches, such as assessing leaves with the help of vein morphology, machine learning allows us to evaluate plants in a way that is much more sophisticated, accurate, and quick.

Soil and Water Management

Machine learning algorithms examine evaporation dynamics, soil moisture, and temperature to comprehend ecosystem processes and their impact on agriculture.

The deficiencies in the soil can be taken care of by ML strategies. For example, machine learning technologies can help farmers maintain optimal amounts of inorganic nitrogen. The nitrogen cycle in the soil and the environment is predicted through nitrogen modeling, which directs the farmer to optimum levels. Software simulations can detect whether nitrogen is available and determine when to add nitrogen to the soil. On the other hand, it can notify the farmer when there is too much nitrogen present, which might damage the crops.

The use of irrigation systems can be made more efficient too, thanks to ML-based applications that estimate daily, weekly, or monthly evapotranspiration and predict the daily dew point temperature, which aids in predicting expected weather events and calculates evapotranspiration and evaporation.

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Yield Prediction and Crop Quality

One of the most significant and well-known areas of precision agriculture is yield prediction, which encompasses mapping and assessment of yields, matching crop supply and demand, and crop management. Modern methods go well beyond simple forecasting based on historical data, incorporating computer vision technologies to deliver data instantly and thorough multidimensional analyses of crops, weather and economic situations to maximize production for farmers and the public at large.

Accurately identifying and categorizing agricultural quality attributes can raise product prices and minimize wastage. In contrast to human specialists, machines can employ seemingly pointless data and connections to expose and discover new attributes that contribute to the overall quality of crops.

Disease and Weed Detection

Significant amounts of pesticides must be sprayed over the crop area to combat disease, which frequently has a high financial cost and a considerable environmental impact. When using general precision agriculture management, ML is used to target the application of agrochemicals based on the time, location and plants that will be affected.

Weeds pose a serious threat to the growth of crops. Weeds are tricky to identify and distinguish from crops, which presents the biggest challenge in weed control. With minimal expense and no negative effects on the environment, computer vision and machine learning algorithms in agriculture can enhance the identification and discrimination of weeds. Future models of this technology will power weed-destroying robots, minimizing the need for herbicides.

Livestock Production and Animal Welfare

To maximize the economic effectiveness of livestock production systems, such as the production of cattle and eggs, machine learning enables precise prediction and prediction of farming aspects. For instance, 150 days before the day of slaughter, weight prediction systems can anticipate future weights, enabling farmers to adjust their diets and environmental factors accordingly.

Today’s livestock is increasingly viewed as animals who can be unhappy and worn out by their life on a farm rather than just as food carriers. Animals’ movement patterns, such as standing, moving, eating, and drinking, can determine how much stress an animal is exposed to and forecast its susceptibility to diseases, weight increase, and productivity. Animals’ chewing signals can be linked to the need for food adjustments.

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Models Used

Agricultural machine learning is not some enigmatic gimmick or magic; rather, it is a collection of well-specified models that gather particular data and employ methodological approaches to get the desired outcome.

Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) are two very popular machine learning models being utilized for agriculture.

ANNs are models of biological neural networks that mimic complex activities like pattern production, reasoning, learning and judgment. They are inspired by how the human brain functions.

SVMs are binary classifiers that divide data instances into categories using a linear separation hyperplane. Clustering, regression and classification are all performed using SVMs. They are utilized in farming to estimate animal production and crop productivity and quality. 

Additionally, Farmer’s Chatbots are now under development. Instead of just providing numbers, these would be able to evaluate the data and consult farmers on complex issues, and hence are predicted to be even smarter than consumer-oriented Alexa and similar assistants.

Conclusion

Machine learning breakthroughs have incredible potential, much like software. Agriculture scientists are putting their theories to the test on a larger scale and assisting in the development of more precise, real-time prediction models pertaining to crops. Machine learning in agriculture has the capacity to provide even more solutions for sustaining the world’s population, coping with climate change and conserving natural resources.

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Currently, machine learning solutions focus on specific issues, but as automated data collection, analysis, and decision-making are further integrated into a connected system, many farming activities will change to what is known to be knowledge-based agriculture, which will be able to boost output and product quality.  


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Next-gen chips, Amazon Q, and speedy S3

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AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print.

Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has been on the AI side for AWS’ announcements, including a major partnership inked with NVIDIA across infrastructure, software, and services.

Yet there has been plenty more announced at the Las Vegas jamboree besides. Here, CloudTech rounds up the best of the rest:

Next-generation chips

This was the other major AI-focused announcement at re:Invent: the launch of two new chips, AWS Graviton4 and AWS Trainium2, for training and running AI and machine learning (ML) models, among other customer workloads. Graviton4 shapes up against its predecessor with 30% better compute performance, 50% more cores and 75% more memory bandwidth, while Trainium2 delivers up to four times faster training than before and will be able to be deployed in EC2 UltraClusters of up to 100,000 chips.

The EC2 UltraClusters are designed to ‘deliver the highest performance, most energy efficient AI model training infrastructure in the cloud’, as AWS puts it. With it, customers will be able to train large language models in ‘a fraction of the time’, as well as double energy efficiency.

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As ever, AWS offers customers who are already utilising these tools. Databricks, Epic and SAP are among the companies cited as using the new AWS-designed chips.

Zero-ETL integrations

AWS announced new Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon Relational Database Services (Amazon RDS) for MySQL integrations with Amazon Redshift, AWS’ cloud data warehouse. The zero-ETL integrations – eliminating the need to build ETL (extract, transform, load) data pipelines – make it easier to connect and analyse transactional data across various relational and non-relational databases in Amazon Redshift.

A simple example of how zero-ETL functions can be seen is in a hypothetical company which stores transactional data – time of transaction, items bought, where the transaction occurred – in a relational database, but use another analytics tool to analyse data in a non-relational database. To connect it all up, companies would previously have to construct ETL data pipelines which are a time and money sink.

The latest integrations “build on AWS’s zero-ETL foundation… so customers can quickly and easily connect all of their data, no matter where it lives,” the company said.

Amazon S3 Express One Zone

AWS announced the general availability of Amazon S3 Express One Zone, a new storage class purpose-built for customers’ most frequently-accessed data. Data access speed is up to 10 times faster and request costs up to 50% lower than standard S3. Companies can also opt to collocate their Amazon S3 Express One Zone data in the same availability zone as their compute resources.  

Companies and partners who are using Amazon S3 Express One Zone include ChaosSearch, Cloudera, and Pinterest.

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Amazon Q

A new product, and an interesting pivot, again with generative AI at its core. Amazon Q was announced as a ‘new type of generative AI-powered assistant’ which can be tailored to a customer’s business. “Customers can get fast, relevant answers to pressing questions, generate content, and take actions – all informed by a customer’s information repositories, code, and enterprise systems,” AWS added. The service also can assist companies building on AWS, as well as companies using AWS applications for business intelligence, contact centres, and supply chain management.

Customers cited as early adopters include Accenture, BMW and Wunderkind.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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HCLTech and Cisco create collaborative hybrid workplaces

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Digital comms specialist Cisco and global tech firm HCLTech have teamed up to launch Meeting-Rooms-as-a-Service (MRaaS).

Available on a subscription model, this solution modernises legacy meeting rooms and enables users to join meetings from any meeting solution provider using Webex devices.

The MRaaS solution helps enterprises simplify the design, implementation and maintenance of integrated meeting rooms, enabling seamless collaboration for their globally distributed hybrid workforces.

Rakshit Ghura, senior VP and Global head of digital workplace services, HCLTech, said: “MRaaS combines our consulting and managed services expertise with Cisco’s proficiency in Webex devices to change the way employees conceptualise, organise and interact in a collaborative environment for a modern hybrid work model.

“The common vision of our partnership is to elevate the collaboration experience at work and drive productivity through modern meeting rooms.”

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Alexandra Zagury, VP of partner managed and as-a-Service Sales at Cisco, said: “Our partnership with HCLTech helps our clients transform their offices through cost-effective managed services that support the ongoing evolution of workspaces.

“As we reimagine the modern office, we are making it easier to support collaboration and productivity among workers, whether they are in the office or elsewhere.”

Cisco’s Webex collaboration devices harness the power of artificial intelligence to offer intuitive, seamless collaboration experiences, enabling meeting rooms with smart features such as meeting zones, intelligent people framing, optimised attendee audio and background noise removal, among others.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: Cisco, collaboration, HCLTech, Hybrid, meetings

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Canonical releases low-touch private cloud MicroCloud

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Canonical has announced the general availability of MicroCloud, a low-touch, open source cloud solution. MicroCloud is part of Canonical’s growing cloud infrastructure portfolio.

It is purpose-built for scalable clusters and edge deployments for all types of enterprises. It is designed with simplicity, security and automation in mind, minimising the time and effort to both deploy and maintain it. Conveniently, enterprise support for MicroCloud is offered as part of Canonical’s Ubuntu Pro subscription, with several support tiers available, and priced per node.

MicroClouds are optimised for repeatable and reliable remote deployments. A single command initiates the orchestration and clustering of various components with minimal involvement by the user, resulting in a fully functional cloud within minutes. This simplified deployment process significantly reduces the barrier to entry, putting a production-grade cloud at everyone’s fingertips.

Juan Manuel Ventura, head of architectures & technologies at Spindox, said: “Cloud computing is not only about technology, it’s the beating heart of any modern industrial transformation, driving agility and innovation. Our mission is to provide our customers with the most effective ways to innovate and bring value; having a complexity-free cloud infrastructure is one important piece of that puzzle. With MicroCloud, the focus shifts away from struggling with cloud operations to solving real business challenges” says

In addition to seamless deployment, MicroCloud prioritises security and ease of maintenance. All MicroCloud components are built with strict confinement for increased security, with over-the-air transactional updates that preserve data and roll back on errors automatically. Upgrades to newer versions are handled automatically and without downtime, with the mechanisms to hold or schedule them as needed.

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With this approach, MicroCloud caters to both on-premise clouds but also edge deployments at remote locations, allowing organisations to use the same infrastructure primitives and services wherever they are needed. It is suitable for business-in-branch office locations or industrial use inside a factory, as well as distributed locations where the focus is on replicability and unattended operations.

Cedric Gegout, VP of product at Canonical, said: “As data becomes more distributed, the infrastructure has to follow. Cloud computing is now distributed, spanning across data centres, far and near edge computing appliances. MicroCloud is our answer to that.

“By packaging known infrastructure primitives in a portable and unattended way, we are delivering a simpler, more prescriptive cloud experience that makes zero-ops a reality for many Industries.“

MicroCloud’s lightweight architecture makes it usable on both commodity and high-end hardware, with several ways to further reduce its footprint depending on your workload needs. In addition to the standard Ubuntu Server or Desktop, MicroClouds can be run on Ubuntu Core – a lightweight OS optimised for the edge. With Ubuntu Core, MicroClouds are a perfect solution for far-edge locations with limited computing capabilities. Users can choose to run their workloads using Kubernetes or via system containers. System containers based on LXD behave similarly to traditional VMs but consume fewer resources while providing bare-metal performance.

Coupled with Canonical’s Ubuntu Pro + Support subscription, MicroCloud users can benefit from an enterprise-grade open source cloud solution that is fully supported and with better economics. An Ubuntu Pro subscription offers security maintenance for the broadest collection of open-source software available from a single vendor today. It covers over 30k packages with a consistent security maintenance commitment, and additional features such as kernel livepatch, systems management at scale, certified compliance and hardening profiles enabling easy adoption for enterprises. With per-node pricing and no hidden fees, customers can rest assured that their environment is secure and supported without the expensive price tag typically associated with cloud solutions.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Tags: automation, Canonical, MicroCloud, private cloud

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