#Agriculture analytics

[aishavass]

Growing government initiatives for adopting enhanced agricultural techniques, coupled with advanced technologies for enhancing crop productivity and data...

[powaraniket]

Global Agriculture Analytics Market is projected to reach USD 2.25 Billion by 2028

Global Agriculture Analytics Market size was valued at USD 1.02 Billion in 2021, and is projected to reach USD 2.25 Billion by 2028, growing at a CAGR of 11.9% from 2022 to 2028.

Agricultural analytics is defined as the use of technologies such as IoT, big data, and other analytical tools in the agricultural sector. Smart farming is part of agricultural analytics and a breakthrough application of science and technology across agriculture. It is used widely to understand different aspects of agriculture such as irrigation and cultivation. Various technologies such as IoT, remote sensing application control, GPS, etc., are used in agricultural analytics.

Read more: https://introspectivemarketresearch.com/reports/agriculture-analytics-market/

[letsvishu]

Agriculture Analytics Market Forecast to Achieve $2.5 Billion by 2028

The global Agriculture Analytics Market is projected to grow from USD 1.4 billion in 2023 to USD 2.5 billion by 2028, reflecting a robust Compound Annual Growth Rate (CAGR) of 13.1%. This growth underscores the increasing adoption of data-driven solutions in agriculture, enhancing productivity and sustainability across the sector.

[agreads]

Launching RegenIQ: A Scalable, Data-Driven Framework Driving the Adoption of Regenerative Agriculture

Agmatix, a leading agricultural data and AI-powered technology company, announces the launch of RegenIQ at the Regenerative Agriculture Summit in Europe. RegenIQ is designed to drive the adoption of regenerative agriculture by offering a structured approach to assessing the impact of field-level efforts, supporting both environmental health and productivity. Aligned with regenerative…

[jcmarchi]

Oleksandr (Sasha) Strozhemin, Co-Founder & CEO of Trinetix – Interview Series

New Post has been published on https://thedigitalinsider.com/oleksandr-sasha-strozhemin-co-founder-ceo-of-trinetix-interview-series/

Oleksandr (Sasha) Strozhemin, Co-Founder & CEO of Trinetix – Interview Series

Oleksandr (Sasha) Strozhemin is a сo-founder and CEO of Trinetix – a global technology company that provides strategy, design, and innovation services to Fortune 500 and fast-growing businesses operating in diverse areas, from finance and professional services to logistics, healthcare, and agriculture.

Leveraging its diverse arsenal of AI and GenAI, intelligent digital assistants, data and analytics, digital workplaces, experience design, and cloud enablement, Trinetix is committed to enabling business leaders to turn their product ideas into competitive, one-of-a-kind market offerings.

To further enable industry-redefining, Trinetix acts as a dedicated technology partner, delivering future-proof strategies and tech talents necessary for scoring transformation goals and driving long-term business outcomes.

Can you share the story behind the founding of Trinetix and your journey to becoming a strategic technology partner for Fortune 100 companies?

I’ve always been fascinated by the pace of innovation, the way new things change our lives with a click. But I’ve also noticed how fast this evolution has led to an overheated market. In 2011, the competition for being the next revolutionary provider and game-changer was extremely intense.

To me, it was a time of opportunity for creating something unique and outstanding. So, I took action.

We started our work in the US in 2011, focusing on AR, experience design, and mobile app development. Of course, AR was a central part of our service offerings because it was a budding trend back then, and it held a promise of creating the exact one-of-a-kind experiences I wanted to deliver.

To find new clients, I took an unorthodox approach. While we targeted the US market, we were particularly interested in companies with a global presence. So, to engage them, we opened a delivery office in Eastern Europe (Kyiv, Ukraine), where we delivered around 20 AR projects for P&G, Nivea, ExxonMobile, and Coca-Cola. It was a great call: in addition to assisting our clients with establishing their presence in the local market, we worked in a region highly responsive to innovation and rich with tech talent.

A year and a half of proactive work passes—and we get an invitation to participate in a tender for experience design for a Fortune 100 enterprise. It turns out they have been monitoring our work closely and added us to the candidates list.

We accept — and out of all the candidates, we are the only ones to give a ready-to-show offer with all the requirements dissected, UI prototypes prepared, and interaction logic animated. The company rep takes our submission to the decision-making group — and we’re in!

In the next two years of developing CX design practice, we smoothly transition to project engineering, taking projects put on deep hiatus and turning them into complete value-driving products.

Naturally, when you work with an industry leader, you find yourself on the threshold of disruptions, navigating and orchestrating transformation. So, our story of embedding AI, intelligent automation, and data analytics into enterprise processes starts at that point.

This experience left me with a deeper understanding of our mission — to guide businesses as they manage digital change and adopt it with maximum impact. This is our commitment.

Trinetix has developed AI chatbots, digital assistants, and AI-powered data intelligence solutions. Can you elaborate on how these technologies are transforming operations for your clients?

In short, I’d say that leveraging AI enables clients to accomplish more in less time—and by more, I mean much, much more.

Today, businesses have a wealth of valuable insights at their fingertips, but finding them requires organizing, categorizing, and validating data. If done by hand, the entire process can take months. Sometimes, there is too much data, and even 10 experts working 24/7 for months won’t suffice.

This is where AI comes into play — and I believe this is its strongest and most game-changing aspect. Replacing months of manual research with instant delivery of the right information for the right objective is simply revolutionary.

How do you ensure that your AI solutions are tailored to meet the specific needs of each client, particularly in diverse industries like logistics and healthcare?

Research comes first. Always.

Any AI model is as strong as the data used for training it and the knowledge of the niche it’s built for.  So, we always start our work with a discovery session. It helps us explore how an enterprise operates, identify its strong points, and study its key competitors.

Our top priority is to put our clients’ vision and needs into features of the future solution—so we also build from their experience, research key enterprise processes, and discuss ways of addressing constraints.

We also establish the general digital dexterity levels across the enterprise and the needs of departments using the technology. That includes helping clients to onboard their teams and providing detailed yet straightforward instructions on operating with the solution.

Can you discuss a recent project where Trinetix integrated generative AI to solve a critical business challenge for a client? What were the key outcomes?

There has been such a case. A Fortune 500 client operating in freight management came to us with a request to transform their request-for-proposal (RPF) management processes.

Since they were handling their RPF tasks manually, they were dealing with slow response times and calculations while accumulating heaps of unstructured data (images, screenshots, emails) — which were never converted into value. Accordingly, great opportunities were either lost among the data or in delayed tasks.

A digital upgrade of key operations was in order.

We developed a multimodal solution that was powered by generative AI, transforming all the unstructured data into a comprehensible source of rich and robust insights. This solution enabled the client to generate quotes straight from the company’s mailboxes and provided end-to-end automation for faster task completion. As a result, the client accelerated their operations, increased their win rates, and optimized quote management, ultimately growing their revenue.

What are the main challenges you face when implementing generative AI solutions, and how do you overcome them?

The way I see it, many challenges stem from the human factor.  For instance, when an enterprise adopts GenAI, employees worry that they are being replaced. As a result, AI meets organizational resistance, which defeats the entire point of technology adoption.

This is where we work together with enterprise leaders, helping them promote the change across their company. I think it’s crucial to address fears from a fact-based angle, providing a realistic perspective on the strengths and weaknesses of the technology. We also include employees in the development process, establishing feedback loops and showing them how the technology works and why it benefits them.

There is also a fear of uncertainty.  Sometimes executives hesitate to proceed because they want to be confident that the ROI will be worth the overhaul. I think communication is once again the key. For instance, our teams always have a designated relationship manager who explains the change to stakeholders, updating them on our progress and the results they should expect.

Your approach emphasizes 360° value and innovation excellence. How do you ensure that your solutions continually provide value and stay ahead of market trends?

We base our work on three principles.

First,  it’s not about the trend but what the client needs. Sometimes, the client comes to us with a presumed solution, but then a discovery session reveals alternatives that better fit their goals. And when we have a fit, we know we have a foundation that will work for the client for years to come.

Second, we base our solution planning on enterprises’ growth, scale, and evolution, so the end product must synergize with these processes. In addition to discussing the client’s potential roadmap and growth trajectory, we also study compatibility, ensuring that the client can integrate new platforms and systems into their framework.

Third, it’s autonomy. While we share responsibility and provide support and success monitoring, we see that clients’ teams can use the product and onboard new users easily without requiring our intervention. So, we approach our solutions from the user POV, anticipating potential issues and introducing measures to prevent them.

What role does intelligent automation play in driving business transformation and innovation for your clients?

Today, we’re observing the arrival of new, flexible business models that embrace change as the only constant. That means rigid models that burden human resources with numerous repetitive routines are becoming a thing of the past.

Intelligent automation enables organizations to make the transition by restructuring their operations and liberating their talents for more complex and impactful tasks. With intelligent automation handling all the processes that can be replicated, a company becomes more agile, gaining a stronger competitive standing and greater confidence in its next steps.

Data underpins many of your services. How does Trinetix help organizations maximize the strategic potential of their data?

Our goal is to connect decision-makers with value-rich insights, enabling them to access necessary data right when needed. That includes removing blindspots, data silos, and bottlenecks by equipping enterprises with every tool they need to dissect information flowing their way.

Instant report generation that converts volumes of complex information into comprehensive storytelling, intuitive dashboards, and fast and responsive AI-powered data analysis tools—we provide all the building blocks for data-fueled strategies.

Can you discuss a success story where your data and analytics services significantly impacted a client’s business operations?

One of our most prominent cases is the business entity research transformation we did for our Fortune 500 client.

Previously, such research was executed by hand—meaning that managers had to send queries across several departments and wait for their response. Then, they had to manually aggregate information collected from over 10 enterprise data systems into a report.

Accordingly, making just one report took months and the risk of human error was high, which led to an inaccurate picture of legal and compliance risks.

We developed a data management system that enabled a single view across all 10+ enterprise sources, providing full visibility of business entity connections and relationships. It allowed managers to generate relevant and accurate reports within days, increasing productivity while reducing the probability of data discrepancies.

What are some emerging trends in AI and digital solutions that you believe will shape the future of enterprise technology?

I’m particularly passionate about advancing and evolving AI assistants into intelligent workplace partners that gather information, facilitate cross-department communication, and enable service personalization by combining machine capacity with human agility.

From my perspective, the integration of AI assistants is going to significantly improve the quality of life for not just customers but also enterprise employees. This will create more dynamic business environments and foster a culture of proactive problem-solving.

Thank you for the great interview, readers who wish to learn more should visit Trinetix.

[dgspeaks]

Harvesting Insights: Harnessing Big Data Analytics for Smarter Agriculture

In today’s digital age, the agricultural sector is undergoing a data revolution thanks to the widespread adoption of big data analytics. From predictive analytics to IoT sensors, big data is transforming every aspect of the farming process, from crop management to supply chain logistics. As we celebrate National Agriculture Week, let’s explore how harnessing big data is reshaping agriculture and…

[mohitbisresearch]

The Europe farm management software and data analytics market (excluding U.K.) was valued at $1,127.1 million in 2022, which is expected to grow with a CAGR of 15.79% and reach $2,345.7 million by 2027. In the European market, farm management software and data analytics solutions are made to improve farmers' operational efficiency and simplify agricultural procedures. By increasing farming methods' openness, these digital solutions lower the likelihood of crop failure. Farmers may easily access all field activities with the use of farm management software, which can be simply accessible through tablets or mobile phones. Insights into farm economics, crop scouting, weather tracking and forecasting, irrigation management, yield monitoring, and field mapping are some of the agricultural industry's primary benefits of these technologies.

[ggacworldwide]

Tech in Action: NPowerFarmers Guide to Implementing Smart Farming Technologies

Greetings, NPowerFarmers! In this installment of the NPowerFarmers Guide, we’re putting technology into action as we explore the practical implementation of smart farming technologies on the farm. From precision agriculture tools to automated machinery, join us as we uncover how these technologies can revolutionize your farming practices and elevate your farm to new heights of efficiency and…

[futuretonext]

According to MarkNtel Advisors’ research report, “North America Agriculture Analytics Market Analysis, 2021,” the market is likely to grow at a CAGR of around 12% in the forecast period of 2021-26 due to the rapid technological advancements and accelerating adoption of advanced farming practices. Moreover, the increasing government initiatives to expand modern agricultural techniques and the rising pressure to meet the food demand are other critical factors likely to fuel the overall market growth in the forecast period.

[luckyonexcel]

Exploring the Future of IoT | Internet of Things

Internet of Things (IoT) has already transformed the way we perceive and interact with technology connecting everyday objects to the digital world. As we navigate through a rapidly evolving technological landscape it becomes crucial to delve deeper into the future of IoT and the endless possibilities it holds. Let’s explore the exciting advancements and emerging trends that will shape the future…

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Top 5 Central Government Scheme For Farmers

Farming is an important sector in India, and the Central Government has introduced several schemes to support farmers and improve their livelihood. These schemes aim to provide financial assistance, agricultural inputs, and other resources to farmers to boost their production and income. In this article, we will discuss the top 5 Central Government schemes for farmers in India.

Pradhan Mantri Fasal Bima Yojana:

The Pradhan Mantri Fasal Bima Yojana (PMFBY) is a crop insurance scheme launched by the Government of India to provide financial support to farmers in case of crop failure due to natural calamities, pests, or diseases. This scheme was launched in 2016 and has since then benefited millions of farmers across the country. Under this scheme, farmers are required to pay a nominal premium, and the rest of the cost is borne by the government. The scheme covers all crops and is available to all farmers who have taken a crop loan or not. PMFBY aims to provide risk management in agriculture and help farmers manage their agricultural risks.

Pradhan Mantri Krishi Sinchai Yojana:

The Pradhan Mantri Krishi Sinchai Yojana (PMKSY) is an irrigation scheme launched by the Government of India in 2015 to provide water to every agricultural field and improve farm productivity. The scheme aims to achieve convergence of investments in irrigation at the field level, expand cultivable area under assured irrigation, and improve on-farm water use efficiency. The scheme also aims to promote sustainable water conservation practices among farmers. PMKSY focuses on creating new irrigation infrastructure and maintaining the existing ones.

National Agriculture Market:

The National Agriculture Market (eNAM) is an online trading platform launched by the Government of India to connect farmers with traders and buyers across the country. eNAM aims to create a unified national market for agricultural commodities by integrating existing Agricultural Produce Market Committee (APMC) markets. This platform provides transparent price discovery and better price realization to farmers. eNAM also helps farmers in selling their products at a competitive price without intermediaries.

Pradhan Mantri Kisan Samman Nidhi Yojana:

The Pradhan Mantri Kisan Samman Nidhi Yojana (PM-Kisan) is a scheme launched by the Government of India in 2019 to provide direct income support to farmers. Under this scheme, small and marginal farmers with less than two hectares of land are eligible to receive income support of Rs 6,000 per year. The scheme aims to provide financial assistance to farmers for meeting their various needs such as purchasing seeds, fertilizers, and other inputs. The scheme is entirely funded by the Central Government and is credited directly into the bank accounts of the beneficiaries.

Paramparagat Krishi Vikas Yojana:

The Paramparagat Krishi Vikas Yojana (PKVY) is a scheme launched by the Government of India in 2015 to promote organic farming in the country. The scheme aims to encourage farmers to adopt eco-friendly and sustainable practices for improving soil health and increasing farm productivity. Under this scheme, farmers are encouraged to form groups and take up organic farming. The government provides financial assistance to these groups for inputs such as bio-fertilizers, bio-pesticides, vermicompost, and other organic inputs. PKVY also provides support for terrace farming and other innovative farming practices.

The government of India has launched various schemes for the welfare of farmers, and these schemes have played a crucial role in the growth and development of the agriculture sector. The schemes mentioned above aim to promote farming activities, enhance crop productivity, and protect the income of farmers from agricultural risks. The schemes also offer several benefits to farmers such as providing financial assistance, promoting the use of modern technologies, and enhancing farming analytics.

It is important to note that these schemes are designed to help farmers, and it is the responsibility of the government to ensure that the benefits reach the targeted audience. It is also essential for farmers to be aware of these schemes and take advantage of them to improve their livelihoods.

Overall, these central government schemes for farmers have been successful in supporting the growth and development of the agriculture sector in India, and they continue to play a significant role in promoting the welfare of farmers in the country.

[jcmarchi]

Harvesting Intelligence: How Generative AI is Transforming Agriculture

New Post has been published on https://thedigitalinsider.com/harvesting-intelligence-how-generative-ai-is-transforming-agriculture/

Harvesting Intelligence: How Generative AI is Transforming Agriculture

In the age of digital transformation, agriculture is no longer just about soil, water, and sunlight. With the advent of generative AI, agriculture is becoming smarter, more efficient, and increasingly data driven. From predicting crop yields with unprecedented accuracy to developing disease-resistant plant varieties, generative AI enables farmers to make precise decisions that optimize yields and resource use. This article examines how generative AI is changing agriculture, looking at its impact on traditional farming practices and its potential for the future.

Understanding Generative AI

Generative AI is a type of artificial intelligence designed to produce new content—whether it’s text, images, or predictive models—based on patterns and examples it has learned from existing data. Unlike traditional AI, which focuses on recognizing patterns or making predictions, generative AI creates original outputs that closely mimic the data it was trained on. This makes it a powerful tool for enhancing decision-making and driving innovation. A key feature of generative AI is to facilitate building AI applications without much labelled training data. This feature is particularly beneficial in fields like agriculture, where acquiring labeled training data can be challenging and costly.

The development of generative AI models involves two main steps: pre-training and fine-tuning. In the pre-training phase, the model is trained on extensive amounts of data to learn general patterns. This process establishes a “foundation” model with broad and versatile knowledge. In the second phase, the pre-trained model is fine-tuned for specific tasks by training it on a smaller, more focused dataset relevant to the intended application, such as detecting crop diseases. These targeted uses of generative AI are referred to as downstream applications. This approach allows the model to perform specialized tasks effectively while leveraging the broad understanding gained during pre-training.

How Generative AI is Transforming Agriculture

In this section, we explore various downstream applications of generative AI in agriculture.

Generative AI as Agronomist Assistant: One of the ongoing issues in agriculture is the lack of qualified agronomists who can offer expert advice on crop production and protection. Addressing this challenge, generative AI can serve as an agronomist assistant by offering farmers immediate expert advice through chatbots. In this context, a recent Microsoft study evaluated how generative AI models, like GPT-4, performed on agriculture-related questions from certification exams in Brazil, India, and the USA. The results were encouraging, showing GPT-4’s ability to handle domain-specific knowledge effectively. However, adapting these models to local, specialized data remains a challenge. Microsoft Research tested two approaches—fine-tuning, which trains models on specific data, and Retrieval-Augmented Generation (RAG), which enhances responses by retrieving relevant documents, reporting these relative advantages.

Generative AI for Addressing Data Scarcity in Agriculture: Another key challenge in applying AI to agriculture is the shortage of labeled training data, which is crucial for building effective models. In agriculture, where labeling data can be labor-intensive and costly, generative AI offers a promising way forward. Generative AI stands out for its ability to work with large amounts of unlabeled historical data, learning general patterns that allow it to make accurate predictions with only a small number of labeled examples. Additionally, it can create synthetic training data, helping to fill gaps where data is scarce. By addressing these data challenges, generative AI improves the performance of AI in agriculture.

Precision Farming: Generative AI is changing precision farming by analyzing data from sources such as satellite imagery, soil sensors, and weather forecasts. It helps with predicting crop yields, automating fruit harvesting, managing livestock, and optimizing irrigation. These insights enable farmers to make better decisions, improving crop health and yields while using resources more efficiently. This approach not only increases productivity but also supports sustainable farming by reducing waste and environmental impact.

Generative AI for Disease Detection: Timely detection of pests, diseases, and nutrient deficiencies is crucial for protecting crops and reducing losses. Generative AI uses advanced image recognition and pattern analysis to identify early signs of these issues. By detecting problems early, farmers can take targeted actions, reduce the need for broad-spectrum pesticides, and minimize environmental impact. This integration of AI in agriculture enhances both sustainability and productivity.

How to Maximize the Impact of Generative AI in Agriculture

While current applications show that generative AI has potential in agriculture, getting the most out of this technology requires developing specialized generative AI models for the field. These models can better understand the nuances of farming, leading to more accurate and useful results compared to general-purpose models. They also adapt more effectively to different farming practices and conditions. The creation of these models, however, involves gathering large amounts of diverse agricultural data—such as crop and pest images, weather data, and insect sounds—and experimenting with different pretraining methods. Although progress is being made, there’s still a lot of work needed to build effective generative AI models for agriculture. Some of the potential use cases of generative AI for agriculture are mentioned below.

Potential Use Cases

A specialized generative AI model for agriculture could open several new opportunities in the field. Some key use cases include:

Smart Crop Management: In agriculture, smart crop management is a growing field that integrates AI, IoT, and big data to enhance tasks like plant growth monitoring, disease detection, yield monitoring, and harvesting. Developing precision crop management algorithms is challenging due to diverse crop types, environmental variables, and limited datasets, often requiring integration of varied data sources such as satellite imagery, soil sensors, and market trends. Generative AI models trained on extensive, multi-domain datasets offer a promising solution, as they can be fine-tuned with minimal examples for various applications. Additionally, multimodal generative AI integrates visual, textual, and sometimes auditory data, providing a comprehensive analytical approach that is invaluable for understanding complex agricultural situations, especially in precision crop management.

Automated Creation of Crop Varieties: Specialized generative AI can transform crop breeding by creating new plant varieties through exploring genetic combinations. By analyzing data on traits like drought resistance and growth rates, the AI generates innovative genetic blueprints and predicts their performance in different environments. This helps identify promising genetic combinations quickly, guiding breeding programs and accelerating the development of optimized crops. This approach aids farmers in adapting to changing conditions and market demands more effectively.

Smart Livestock Farming: Smart livestock farming leverages IoT, AI, and advanced control technologies to automate essential tasks like food and water supply, egg collection, activity monitoring, and environmental management. This approach aims to boost efficiency and cut costs in labor, maintenance, and materials. The field faces challenges due to the need for expertise across multiple fields and labor-intensive job. Generative AI could address these challenges by integrating extensive multimodal data and cross-domain knowledge, helping to streamline decision-making and automate livestock management.

Agricultural robots: Agricultural robots are transforming modern farming by automating tasks such as planting, weeding, harvesting, and monitoring crop health. AI-guided robots can precisely remove weeds and drones with advanced sensors can detect diseases and pests early, reducing yield losses. Developing these robots requires expertise in robotics, AI, plant science, environmental science, and data analytics, handling complex data from various sources. Generative AI offers a promising solution for automating various tasks of agricultural robots by providing advanced vision, predictive, and control capabilities.

 The Bottom Line

Generative AI is reshaping agriculture with smarter, data-driven solutions that improve efficiency and sustainability. By enhancing crop yield predictions, disease detection, and crop breeding, this technology is transforming traditional farming practices. While current applications are promising, the real potential lies in developing specialized AI models tailored to the unique needs of agriculture. As we refine these models and integrate diverse data, we can unlock new opportunities to help farmers optimize their practices and better navigate the challenges of modern farming.