#ai in agriculture industry

[techdriveplay]

What Is the Future of Robotics in Everyday Life?

As technology continues to evolve at a rapid pace, many are asking, what is the future of robotics in everyday life? From automated vacuum cleaners to advanced AI assistants, robotics is steadily becoming an integral part of our daily routines. The blending of artificial intelligence with mechanical engineering is opening doors to possibilities that seemed like science fiction just a decade…

[brewscoop]

Discover how the nation's #1 brewer, Anheuser-Busch, is championing American farmers with the US Farmed Certification! Learn how this initiative supports local agriculture, ensures high-quality ingredients, and boosts sustainability. Check out the full story on how these efforts are shaping the future of US agriculture.

[jcmarchi]

1000 AI-powered machines: Vision AI on an industrial scale

New Post has been published on https://thedigitalinsider.com/1000-ai-powered-machines-vision-ai-on-an-industrial-scale/

1000 AI-powered machines: Vision AI on an industrial scale

This article is based on Bart Baekelandt’s brilliant talk at the Computer Vision Summit in London. Pro and Pro+ members can enjoy the complete recording here. For more exclusive content, head to your membership dashboard. 

Hi, I’m Bart Baekelandt, Head of Product Management at Robovision. 

Today, we’re going to talk about the lessons we’ve learned over the last 15 years of applying AI to robots and machines at scale in the real world. 

Robovision’s journey: From flawed to fantastic

First, let’s look at what these machines were like in the past.

15 years ago, our machine was very basic with extremely rudimentary computer vision capabilities. It used classical machine vision techniques and could only handle very basic tasks like recognizing a hand. Everything was hard-coded, so if you needed the machine to recognize something new, you’d have to recode the entire application. It was expensive and required highly skilled personnel.

Nowadays, we don’t have just one machine – we have entire populations of machines, with advanced recognition capabilities. There’s a continuous process of training AI models and applying them to production so the machines can tackle the problem at hand.

For example, there are machines that can take a seedling from a conveyor belt and plant it in a tray. We have entire fleets of these specialized machines. One day they’re trained to handle one type of seedling, and the next day they’re retrained to perform optimally for a different variety of plant. 

So yeah, a lot has happened in 15 years. We’ve gone from initially failing to scale AI, to figuring out how to apply AI at scale with minimal support from our side. As of today, we’ve produced over 1000 machines with game-changing industrial applications 

Let’s dive into a few of the key lessons we’ve picked up along the way.

Lesson one: AI success happens after the pilot

The first lesson is that AI success happens after the pilot phase. We learned this lesson the hard way in the initial stages of applying AI, around 2012.

Let me share a quick anecdote. When we were working on the machine that takes seedlings from a conveyor belt and plants them in trays, we spent a lot of time applying AI and building the algorithm to recognize the right breaking point on each seedling and plant it properly. 

Eventually, we nailed it – the algorithm worked perfectly. The machine builder who integrated it was happy, and the customer growing the seedlings was delighted because everything was functioning as intended.

However, the congratulations were short-lived. Within two weeks, we got a call – the system wasn’t picking the seedlings well anymore. What had happened? They were now trying to handle a different seedling variety, and the images looked just different enough that our AI model struggled. The robot started missing the plants entirely or planting them upside down.

We got new image data from the customer’s operations and retrained the model. Great, it worked again! But sure enough, two weeks later, we got another call reporting the same problem all over again. 

This highlighted a key problem. The machine builder wanted to sell to many customers, but we couldn’t feasibly support each one by perpetually retraining models on their unique data. That approach doesn’t scale. 

That painful lesson was the genesis of our products. We realized the end customers needed to be able to continuously retrain the models themselves without our assistance. So, we developed tooling for them to capture new data, convert it to retrained models, deploy those models to the machines, and interface with the machines for inference. 

Our product philosophy stems directly from those harsh real-world lessons about what’s required to successfully scale AI in real-world production.

Lesson two: It’s about getting the odd couple to work together

When you’re creating working AI solutions at scale, there typically are two types of people involved. They’re your classic “odd couple,” but they need to be able to collaborate effectively.

On one hand, you have the data scientists – they generally have advanced degrees like Masters in Engineering or even PhDs. Data scientists are driven by innovation. They live to solve complex problems and find solutions to new challenges. 

Once they’ve cracked the core issue, however, they tend to lose interest. They want to move on to the next big innovation, rather than focusing on continuous improvement cycles or incremental optimizations

On the other hand, you have the machine operators who run the manufacturing systems and processes where AI gets applied at scale – whether that’s a factory, greenhouse, or another facility. 

The machine operators have intricate knowledge of the products being handled by the machines. If you’re deploying AI to handle seedlings, for example, no one understands the nuances, variations, and defects of those plants better than the operator.

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[kenresearch1]

Discover the Future of Farming: Global AI in Agriculture Industry is transforming agriculture through advanced technology and industry leaders like IBM and John Deere.

[greenthestral]

The Digital Green Economy: Paving the Way for a Sustainable Future

In recent years, the global community has witnessed a growing sense of urgency in addressing the pressing challenges posed by climate change and environmental degradation. Governments, organizations, and individuals have come to recognize the need for sustainable practices and innovative solutions to mitigate the impact of these issues. As a result, the concept of a green economy has gained significant traction and has become a focal point for discussions on sustainability and economic growth.

A green economy refers to an economic system that prioritizes environmental sustainability, resource efficiency, and social well-being. It aims to decouple economic growth from resource consumption and environmental degradation, instead promoting sustainable development that meets the needs of the present without compromising the ability of future generations to meet their own needs. The principles of a green economy include transitioning to renewable energy sources, promoting sustainable production and consumption patterns, and investing in green technologies and infrastructure.

However, as we enter further into the digital age, another powerful force has emerged—the digital green economy. This innovative approach combines the principles of sustainability with the transformative power of technology, paving the way for even more profound changes and opportunities.

The digital green economy harnesses the potential of digital technologies to drive sustainable development. It leverages advancements in areas such as artificial intelligence, the Internet of Things, data analytics, and cloud computing to create intelligent systems that optimize resource use, enhance energy efficiency, and reduce environmental impact.

One of the key advantages of the digital green economy is its ability to collect, analyze, and interpret vast amounts of data in real-time. The Internet of Things (IoT) enables the connection of various devices and sensors, allowing for the monitoring and control of energy consumption, waste management, and water usage. This level of connectivity and data-driven insights enable businesses and individuals to identify inefficiencies and make informed decisions that contribute to sustainability.

Artificial intelligence and machine learning algorithms are also pivotal in the digital green economy. These technologies can analyze complex datasets, identify patterns, and predict trends, allowing businesses to optimize their operations, reduce waste, and develop innovative solutions. For example, AI algorithms can optimize transportation routes, reducing fuel consumption and emissions, or predict energy demand, enabling renewable energy systems to adjust accordingly.

The digital green economy offers numerous advantages that contribute to shaping a sustainable future. Firstly, it helps reduce environmental impact. By leveraging digital technologies, businesses can lower their carbon footprint and minimize their use of natural resources. Smart grids, for instance, optimize energy distribution, reducing energy losses and dependence on fossil fuels. Additionally, remote working and teleconferencing technologies decrease the need for business travel, thus reducing transportation-related emissions.

Secondly, the digital green economy promotes resource conservation and efficiency. By using data-driven insights, companies can identify areas of improvement, enhance energy and water efficiency, and minimize material waste. This fosters a circular economy approach, where resources are utilized and reused in a sustainable manner, reducing the strain on the environment.

Moreover, the digital green economy presents significant economic opportunities. As businesses embrace sustainable practices and develop green technologies, new markets and industries emerge. The transition to renewable energy sources, for example, creates jobs in the renewable energy sector, clean technology development, and green infrastructure. This not only drives economic growth but also ensures that sustainability becomes a cornerstone of future prosperity.

Additionally, the digital green economy enhances resilience and adaptability in the face of climate change and other environmental challenges. By diversifying energy sources and embracing decentralized systems, communities can become more self-sufficient and less vulnerable to disruptions. The integration of renewable energy sources and microgrids, for example, can provide reliable power even during natural disasters, ensuring the continuous functioning of critical infrastructure.

Numerous digital green economy initiatives are already underway worldwide, demonstrating the potential of this transformative approach. Smart cities, for instance, leverage digital technologies to enhance urban sustainability. These initiatives integrate IoT devices, data analytics, and AI to optimize resource usage, improve transportation systems, and enhance citizen services. Barcelona's implementation of a smart irrigation system, adjusting watering schedules based on weather data to reduce water consumption in public parks, exemplifies the impact of such initiatives.

Furthermore, the integration of renewable energy sources into the existing energy grid is another significant aspect of the digital green economy. Through the use of smart grids and advanced energy management systems, renewable energy generation can be optimized and balanced with demand. Germany's Energiewende is a prime example, where digital technologies enable the efficient integration of wind and solar power into the national energy mix.

Precision agriculture is yet another domain where digital technologies are revolutionizing the sector and promoting sustainable practices. Precision agriculture utilizes sensors, drones, and AI algorithms to monitor crop health, optimize irrigation, and reduce the use of pesticides and fertilizers. This not only minimizes environmental impact but also enhances crop yields and farmer profitability.

However, as we delve into the potential of the digital green economy, it is essential to address certain challenges to ensure its widespread adoption and inclusivity. One of the primary challenges is the digital divide. Access to digital technologies and connectivity remains uneven globally, with underserved populations lacking the necessary infrastructure and skills. Bridging this divide is crucial to ensure that all communities can benefit from the digital green economy. Governments, businesses, and organizations must work together to improve internet access and provide training and support to ensure equal participation.

Another challenge is data privacy and security. The digital green economy relies on vast amounts of data to drive sustainable practices. It is imperative to establish robust cybersecurity measures and transparent data governance frameworks to protect sensitive information and maintain public trust.

Furthermore, the rapid proliferation of digital technologies also leads to an increase in electronic waste (e-waste). Proper e-waste management practices must be implemented to minimize environmental harm. This includes establishing recycling programs, promoting responsible disposal methods, and designing products that are durable and repairable.

The digital green economy represents a promising pathway towards a sustainable future. By leveraging digital technologies and integrating sustainable practices, we can reduce environmental impact, enhance resource efficiency, foster economic growth, and enhance resilience. The digital green economy offers numerous advantages, including reduced environmental impact, resource conservation, economic growth, and enhanced resilience. However, it is crucial to address challenges such as the digital divide, data privacy concerns, and e-waste management to ensure inclusivity and long-term success. By embracing the digital green economy, we can pave the way for a more sustainable and resilient world.

Defining the Digital Green Economy

The digital green economy refers to the integration of digital technologies and sustainable practices to promote environmentally friendly and resource-efficient solutions. It encompasses a wide range of sectors, including renewable energy, smart cities, circular economy, sustainable agriculture, and green transportation. The key objective is to leverage digital advancements to minimize environmental impact, reduce carbon emissions, and enhance resource conservation.

The Role of Digital Technologies

Digital technologies play a crucial role in driving the transition to a green economy. They enable the collection, analysis, and interpretation of vast amounts of data, facilitating informed decision-making and resource optimization. For instance, the Internet of Things (IoT) allows for real-time monitoring and control of energy consumption, waste management, and water usage, enabling businesses and individuals to identify and rectify inefficiencies.

Moreover, artificial intelligence (AI) and machine learning algorithms can analyze complex datasets to identify patterns and predict trends. This enables businesses to optimize their operations, reduce waste, and develop innovative solutions. For example, AI-powered algorithms can optimize transportation routes, reducing fuel consumption and emissions, or predict energy demand, enabling renewable energy systems to adjust accordingly.

Advantages of the Digital Green Economy

The digital green economy offers several advantages that contribute to a sustainable future:

Environmental Impact Reduction: By harnessing digital technologies, businesses can reduce their carbon footprint and environmental impact. For instance, smart grids can optimize energy distribution, reducing energy losses and reliance on fossil fuels. Additionally, remote working and teleconferencing technologies can decrease the need for business travel, lowering transportation-related emissions.

Resource Conservation: The digital green economy promotes resource efficiency by optimizing processes and reducing waste generation. Through data-driven insights, companies can identify areas of improvement, enhance energy and water efficiency, and minimize material waste. This fosters a circular economy approach, where resources are utilized and reused in a sustainable manner.

Economic Growth and Job Creation: The digital green economy presents significant opportunities for economic growth and job creation. As businesses embrace sustainable practices and develop innovative green technologies, new markets and industries emerge. This leads to the creation of jobs in sectors such as renewable energy, clean technology, and green infrastructure development.

Resilience and Adaptability: The digital green economy enhances resilience and adaptability in the face of climate change and other environmental challenges. By diversifying energy sources and embracing decentralized systems, communities can become more self-sufficient and less vulnerable to disruptions. For example, the integration of renewable energy sources and microgrids can provide reliable power even during natural disasters.

Examples of Digital Green Economy Initiatives

Numerous digital green economy initiatives are already underway worldwide, showcasing the potential of this transformative approach:

Smart Cities: Cities around the globe are leveraging digital technologies to enhance urban sustainability. Smart city initiatives integrate IoT devices, data analytics, and AI to optimize resource usage, improve transportation systems, and enhance citizen services. For example, Barcelona has implemented a smart irrigation system that adjusts watering schedules based on weather data, reducing water consumption in public parks.

Renewable Energy Integration: The digital green economy facilitates the integration of renewable energy sources into the existing energy grid. Through smart grids and advanced energy management systems, renewable energy generation can be optimized and balanced with demand. Germany's Energiewende is a prime example, where digital technologies enable the efficient integration of wind and solar power into the national energy mix.

Precision Agriculture: Digital technologies are revolutionizing the agricultural sector by promoting sustainable and resource-efficient practices. Precision agriculture utilizes sensors, drones, and AI algorithms to monitor crop health, optimize irrigation, and reduce the use of pesticides and fertilizers. This not only minimizes environmental impact but also enhances crop yields and farmer profitability.

Overcoming Challenges and Ensuring Inclusivity

While the digital green economy holds immense potential, it is essential to address certain challenges to ensure its widespread adoption and inclusivity. These challenges include:

Digital Divide: Access to digital technologies and connectivity remains uneven globally. Bridging the digital divide is crucial to ensure that all communities can benefit from the digital green economy. Governments, businesses, and organizations must work together to improve internet access and provide training and support for underserved populations.

Data Privacy and Security: As the digital green economy relies on vast amounts of data, ensuring data privacy and security is paramount. Robust cybersecurity measures and transparent data governance frameworks must be in place to protect sensitive information and maintain public trust.

E-Waste Management: The rapid proliferation of digital technologies also leads to an increase in electronic waste. Proper e-waste management practices must be implemented to minimize environmental harm. This includes recycling programs, responsible disposal methods, and product design that promotes durability and repairability.

Conclusion

The digital green economy represents a promising pathway towards a sustainable future. By leveraging digital technologies and sustainable practices, we can reduce environmental impact, enhance resource efficiency, and foster economic growth. From smart cities to renewable energy integration and precision agriculture, numerous initiatives exemplify the transformative power of the digital green economy. However, it is crucial to overcome challenges such as the digital divide, data privacy concerns, and e-waste management to ensure inclusivity and long-term success. By embracing the digital green economy, we can pave the way for a more sustainable and resilient world.

[mobiloitte7]

Empowering Agriculture with Digital Solutions

Revolutionize the agriculture industry with Mobiloitte's advanced IT solutions. Leverage the power of Blockchain, AI, IoT, and the Metaverse to optimize farming processes, increase yields, and ensure sustainability. Our mobile and web apps, along with DevOps and cloud expertise, empower seamless operations and data-driven decision-making. Embrace the digital era and unlock the potential of agriculture with Mobiloitte.

[mohitbisresearch]

Artificial Intelligence in Agriculture Market is expected to reach $4,096.1 million in 2027, AI in Agriculture Industry following a CAGR of 21.98% during 2022-2027.

[ashimbisresearch]

Comprehensive Research Forecast: Artificial Intelligence in Agriculture Market | BIS Research

The intersection of agriculture and artificial intelligence (AI) is reshaping the landscape of modern farming, ushering in an era of precision, efficiency, and sustainable practices.

The Rise of AI in Agriculture

Artificial intelligence has emerged as a transformative force in agriculture, offering solutions to age-old challenges and introducing unprecedented efficiency. The integration of AI technologies in farming practices holds the promise of optimized resource utilization, improved crop yields, and environmentally conscious cultivation.

The Global Artificial Intelligence in Agriculture Market was valued at $1,517.0 million in 2022 and is expected to reach $4,096.1 million in 2027, following a CAGR of 21.98% during 2022-2027.

Key Trends Shaping the Landscape

Precision Farming Revolution:

AI empowers precision farming by analyzing vast datasets, offering insights into crop health, soil conditions, and weather patterns.

Precision agriculture practices, guided by AI algorithms, enhance decision-making for optimal resource allocation and yield maximization.

Crop Monitoring and Disease Detection:

AI-powered sensors and imaging technologies enable real-time crop monitoring.

Advanced algorithms detect early signs of diseases, allowing farmers to implement timely interventions and minimize crop losses.

Autonomous Machinery and Robotics:

AI-driven autonomous machinery, including drones and robotic systems, is revolutionizing farm operations.

Automation of tasks such as planting, harvesting, and weeding enhances operational efficiency and reduces labor dependency.

Click on the link to download free insight on Artificial Intelligence in the Agriculture Industry.

Challenges and Solutions

Data Security and Privacy Concerns:

The influx of data in AI-driven agriculture raises concerns about data security and privacy.

Industry stakeholders are actively addressing these challenges through robust cybersecurity measures and transparent data handling practices.

Accessibility and Adoption Barriers:

While AI holds immense potential, ensuring its accessibility to all farmers remains a challenge.

Educational initiatives, government support, and collaborative efforts are crucial in overcoming barriers to AI adoption in agriculture.

Regional Dynamics

North America's Tech Prowess:

North America leads in AI adoption, with tech-savvy farmers embracing advanced solutions.

Government initiatives and strong technological infrastructure contribute to the region's prominence in the AI in Agriculture Market.

Asia-Pacific's Growing Landscape:

The Asia-Pacific region is witnessing a surge in AI adoption as awareness grows.

Increasing support for sustainable agriculture practices and the need for food security are driving the market in this region.

Future Outlook and Innovations

Integration with IoT and Big Data:

The synergy between AI, Internet of Things (IoT), and Big Data is a key trend shaping the future.

Real-time data from connected devices enhances AI capabilities, fostering more informed decision-making.

Advancements in Machine Learning:

Machine learning algorithms are evolving rapidly, offering more sophisticated analyses of agricultural data.

Continuous advancements in AI-driven machine learning contribute to the refinement of predictive modeling and crop management.

Conclusion

The Comprehensive Research Forecast on the Artificial Intelligence in Agriculture Market paints a vivid picture of an industry in flux, embracing innovation to meet the demands of a rapidly evolving world. As AI continues to carve its path in agriculture, the synergy between technology and cultivation promises a future where precision and sustainability go hand in hand.

[rwby-encrusted-blog]

There are two types of Mech, the Analog, and the Linked.

Analog mechs are the far, far cheaper of the two. Much less specialized, and infinitely more common, able to be found not just in military use, but in Agricultural and Industrial use as well.

To pilot one, all you need it four limbs (Including prosthetics) and good senses, and to score well in the Simulators to get your license.

Analog is a bit of a misnomer, a holdover word from yesteryear, simply meaning "Less sophisticated."

Analog Mechs work by scanning the Brain Activity of the pilot, comparing it to the physical input of the controls, and deciding what to do from there. They're considered old fashioned and less reliable than Linked Mechs, but they're reliable enough for the work they do. They are more easily replaced and retrofitted for different tasks.

They have simpler AI, and to exit one, you simply take the helmet off and open the hatch.

Linked Mechs are a whole other can of worms.

Each and every Linked Mech is custom built and fitted to their pilot, from the heaviest weapon, to the smallest nut, no one is the same as the other.

Linked Mech are Physically plugged into their pilot's nervous system, and they act as one. It can take hours of Decompression and Disconnection to remove a Linked Pilot.

Linked Mechs are truly nothing without their pilots.

Linked Pilots are the most terrifying people you may ever meet.

Their skin is mottled from the Oxygenator-Coolant that runs in their veins, the plugs and ports too deeply entwined in their flesh and body to be removed without serious disruption to their faculties, hairless and sterile from their conditioning and actions when inside their vessel, and that's only the physical differences.

Depending on how long they've been a Linked Pilot, their mental capabilities are affected in different ways.

'Young' pilots are simply too wary - able to pick up on the most minute details, in all five senses.

Their Mechs are still machine.

'Moderate' pilots have some neurological and mental degradation in addition to what they had before. often confused or forgetful outside of their mech, but still very aware of their surroundings, if not their place in time.

Their Mechs act protective of them, like a dog to it's beloved owner.

'Old' Pilots have all the earlier issues, compounded and worsened, and sometimes confusing themself for their mech, even going so far as to entering refill and Refueling areas of the hangars.

Their Mechs act the same, often trying to reach their pilot's quarters and the Cafeteria.

They may refuse to separate at time, feeling more comfortable together than apart.

and then there are the 'Fused' the oldest and rarest caste of Linked Pilot and Mech.

The Pilot and The Mech fused absolutely, inseparable for Neurological, Psychological, and physical reasons.

They are the same. They are one. A perfect fusion of the Biological and Mechanical, Electrochemical Intelligence and the Fissile-Logic Personality, Mirroring each other with every breath and ventilation protocol.

And they are still people.

The Fused still need social interaction and entertainment. They still need variety and novelty, comfort. All the things anyone else would.

Fused are the least common, but are the least likely to die. Too perfectly combined, Too well accompanied.

The Three times a 'Fused' has been separated, The pilot Died, the Mech 'Bricked' itself, like a Jail-broke phone, no matter how it was done.

Fused are the most loyal, true, and caring of all pilots.

Few ever meet the 'Pilot,' the meat within the metal, but the few that do are those most trusted by the Fused person, as the Fused is showing you their beating heart.

If you earn one's trust, and are given this deep, grand honor, I only have one piece of advice.

Do. Not. Break. That. Trust.

[allthecanadianpolitics]

The federal government will be investing $2.4 billion to accelerate Canada’s artificial intelligence (AI) sector, Prime Minister Justin Trudeau announced Sunday. The investment will be divided between a number of measures meant to advance job growth in the AI and tech industry and boost businesses’ productivity. “This announcement is a major investment in our future, in the future of workers, in making sure that every industry, and every generation, has the tools to succeed and prosper in the economy of tomorrow,” Trudeau said in a press release Sunday. Majority of the funds, $2 billion, will go toward increasing access to computing and technological infrastructure. Another $200 million is being invested into AI start-ups to accelerate the technology in “critical sectors” such as health care, agriculture and manufacturing, the release says. Additional funds will be put toward helping small and medium-sized businesses incorporate AI, with another $50 million being committed to help train workers whose jobs may be disrupted by the technology.

Continue Reading.

Tagging: @politicsofcanada

[doomdoomofdoom]

I'm an american studies major (genuinely)

Drop a reference to agriculture in there and you've 100% nailed it.

I know that dissing art is probably a bad idea on the Artists Website but American folk art looks pretty easy conceptually. You just paint like a little cottage on a green hillside with a garden and a rusty truck and a flagpole. And boom that's Americana baby. If I knew how to paint I could be doing that.

[probablyasocialecologist]

The rapid growth of the technology industry and the increasing reliance on cloud computing and artificial intelligence have led to a boom in the construction of data centers across the United States. Electric vehicles, wind and solar energy, and the smart grid are particularly reliant on data centers to optimize energy utilization. These facilities house thousands of servers that require constant cooling to prevent overheating and ensure optimal performance. Unfortunately, many data centers rely on water-intensive cooling systems that consume millions of gallons of potable (“drinking”) water annually. A single data center can consume up to 3 million to 5 million gallons of drinking water per day, enough to supply thousands of households or farms. The increasing use and training of AI models has further exacerbated the water consumption challenges faced by data centers.

[...]

The drinking water used in data centers is often treated with chemicals to prevent corrosion and bacterial growth, rendering it unsuitable for human consumption or agricultural use. This means that not only are data centers consuming large quantities of drinking water, but they are also effectively removing it from the local water cycle. Dry air reduces the risk of corrosion and electrical issues in the sensitive equipment in the data centers. The lack of humidity in water-stressed regions, such as the American Southwest, makes it an attractive location for data centers. This means that the regions in which it is “best” to locate a data center due to its arid environment has the highest marginal cost in terms of water consumption. In the Phoenix area alone, there are 58 data centers. If each data center uses 3 million gallons of water per day for cooling, that equates to over 170 million gallons of drinking water used per day for cooling data centers. This massive consumption of drinking water for data center cooling puts a strain on the already fragile water supply and raises ethical questions about prioritizing the needs of tech giants over the basic needs of residents and agriculture.

[...]

Optimizing renewable power with AI and data centers at the expense of increasing water consumption is not a sustainable solution. Prioritizing one aspect of sustainability, such as reducing carbon emissions, while neglecting another crucial resource like water creates an illusion of sustainability. In reality, this can lead to unsustainable practices that can have severe unintended consequences for individuals and farmers, especially in water-stressed regions.

[jcmarchi]

Reducing pesticide use while increasing effectiveness

New Post has been published on https://thedigitalinsider.com/reducing-pesticide-use-while-increasing-effectiveness/

Reducing pesticide use while increasing effectiveness

Farming can be a low-margin, high-risk business, subject to weather and climate patterns, insect population cycles, and other unpredictable factors. Farmers need to be savvy managers of the many resources they deal, and chemical fertilizers and pesticides are among their major recurring expenses.

Despite the importance of these chemicals, a lack of technology that monitors and optimizes sprays has forced farmers to rely on personal experience and rules of thumb to decide how to apply these chemicals. As a result, these chemicals tend to be over-sprayed, leading to their runoff into waterways and buildup up in the soil.

That could change, thanks to a new approach of feedback-optimized spraying, invented by AgZen, an MIT spinout founded in 2020 by Professor Kripa Varanasi and Vishnu Jayaprakash SM ’19, PhD ’22.

Play video

AgZen has developed a system for farming that can monitor exactly how much of the sprayed chemicals adheres to plants, in real time, as the sprayer drives through a field. Built-in software running on a tablet shows the operator exactly how much of each leaf has been covered by the spray.

Over the past decade, AgZen’s founders have developed products and technologies to control the interactions of droplets and sprays with plant surfaces. The Boston-based venture-backed company launched a new commercial product in 2024 and is currently piloting another related product. Field tests of both have shown the products can help farmers spray more efficiently and effectively, using fewer chemicals overall.

“Worldwide, farms spend approximately $60 billion a year on pesticides. Our objective is to reduce the number of pesticides sprayed and lighten the financial burden on farms without sacrificing effective pest management,” Varanasi says.

Getting droplets to stick

While the world pesticide market is growing rapidly, a lot of the pesticides sprayed don’t reach their target. A significant portion bounces off the plant surfaces, lands on the ground, and becomes part of the runoff that flows to streams and rivers, often causing serious pollution. Some of these pesticides can be carried away by wind over very long distances.

“Drift, runoff, and poor application efficiency are well-known, longstanding problems in agriculture, but we can fix this by controlling and monitoring how sprayed droplets interact with leaves,” Varanasi says.

With support from MIT Tata Center and the Abdul Latif Jameel Water and Food Systems Lab, Varanasi and his team analyzed how droplets strike plant surfaces, and explored ways to increase application efficiency. This research led them to develop a novel system of nozzles that cloak droplets with compounds that enhance the retention of droplets on the leaves, a product they call EnhanceCoverage.

Field studies across regions — from Massachusetts to California to Italy and France —showed that this droplet-optimization system could allow farmers to cut the amount of chemicals needed by more than half because more of the sprayed substances would stick to the leaves.

Measuring coverage

However, in trying to bring this technology to market, the researchers faced a sticky problem: Nobody knew how well pesticide sprays were adhering to the plants in the first place, so how could AgZen say that the coverage was better with its new EnhanceCoverage system?

“I had grown up spraying with a backpack on a small farm in India, so I knew this was an issue,” Jayaprakash says. “When we spoke to growers, they told me how complicated spraying is when you’re on a large machine. Whenever you spray, there are so many things that can influence how effective your spray is. How fast do you drive the sprayer? What flow rate are you using for the chemicals? What chemical are you using? What’s the age of the plants, what’s the nozzle you’re using, what is the weather at the time? All these things influence agrochemical efficiency.”

Agricultural spraying essentially comes down to dissolving a chemical in water and then spraying droplets onto the plants. “But the interaction between a droplet and the leaf is complex,” Varanasi says. “We were coming in with ways to optimize that, but what the growers told us is, hey, we’ve never even really looked at that in the first place.”

Although farmers have been spraying agricultural chemicals on a large scale for about 80 years, they’ve “been forced to rely on general rules of thumb and pick all these interlinked parameters, based on what’s worked for them in the past. You pick a set of these parameters, you go spray, and you’re basically praying for outcomes in terms of how effective your pest control is,” Varanasi says.

Before AgZen could sell farmers on the new system to improve droplet coverage, the company had to invent a way to measure precisely how much spray was adhering to plants in real-time.

Comparing before and after

The system they came up with, which they tested extensively on farms across the country last year, involves a unit that can be bolted onto the spraying arm of virtually any sprayer. It carries two sensor stacks, one just ahead of the sprayer nozzles and one behind. Then, built-in software running on a tablet shows the operator exactly how much of each leaf has been covered by the spray. It also computes how much those droplets will spread out or evaporate, leading to a precise estimate of the final coverage.

“There’s a lot of physics that governs how droplets spread and evaporate, and this has been incorporated into software that a farmer can use,” Varanasi says. “We bring a lot of our expertise into understanding droplets on leaves. All these factors, like how temperature and humidity influence coverage, have always been nebulous in the spraying world. But now you have something that can be exact in determining how well your sprays are doing.”

“We’re not only measuring coverage, but then we recommend how to act,” says Jayaprakash, who is AgZen’s CEO. “With the information we collect in real-time and by using AI, RealCoverage tells operators how to optimize everything on their sprayer, from which nozzle to use, to how fast to drive, to how many gallons of spray is best for a particular chemical mix on a particular acre of a crop.”

The tool was developed to prove how much AgZen’s EnhanceCoverage nozzle system (which will be launched in 2025) improves coverage. But it turns out that monitoring and optimizing droplet coverage on leaves in real-time with this system can itself yield major improvements.

“We worked with large commercial farms last year in specialty and row crops,” Jayaprakash says. “When we saved our pilot customers up to 50 percent of their chemical cost at a large scale, they were very surprised.” He says the tool has reduced chemical costs and volume in fallow field burndowns, weed control in soybeans, defoliation in cotton, and fungicide and insecticide sprays in vegetables and fruits. Along with data from commercial farms, field trials conducted by three leading agricultural universities have also validated these results.

“Across the board, we were able to save between 30 and 50 percent on chemical costs and increase crop yields by enabling better pest control,” Jayaprakash says. “By focusing on the droplet-leaf interface, our product can help any foliage spray throughout the year, whereas most technological advancements in this space recently have been focused on reducing herbicide use alone.” The company now intends to lease the system across thousands of acres this year.

And these efficiency gains can lead to significant returns at scale, he emphasizes: In the U.S., farmers currently spend $16 billion a year on chemicals, to protect about $200 billion of crop yields.

The company launched its first product, the coverage optimization system called RealCoverage, this year, reaching a wide variety of farms with different crops and in different climates. “We’re going from proof-of-concept with pilots in large farms to a truly massive scale on a commercial basis with our lease-to-own program,” Jayaprakash says.

“We’ve also been tapped by the USDA to help them evaluate practices to minimize pesticides in watersheds,” Varanasi says, noting that RealCoverage can also be useful for regulators, chemical companies, and agricultural equipment manufacturers.

Once AgZen has proven the effectiveness of using coverage as a decision metric, and after the RealCoverage optimization system is widely in practice, the company will next roll out its second product, EnhanceCoverage, designed to maximize droplet adhesion. Because that system will require replacing all the nozzles on a sprayer, the researchers are doing pilots this year but will wait for a full rollout in 2025, after farmers have gained experience and confidence with their initial product.

“There is so much wastage,” Varanasi says. “Yet farmers must spray to protect crops, and there is a lot of environmental impact from this. So, after all this work over the years, learning about how droplets stick to surfaces and so on, now the culmination of it in all these products for me is amazing, to see all this come alive, to see that we’ll finally be able to solve the problem we set out to solve and help farmers.”

[southernsolarpunk]

Hey what the fuck is this news story?

“ But the world’s largest economies are already there: The total fertility rate among the OECD’s 38 member countries dropped to just 1.5 children per woman in 2022 from 3.3 children in 1960. That’s well below the “replacement level” of 2.1 children per woman needed to keep populations constant.

That means the supply of workers in many countries is quickly diminishing.

In the 1960s, there were six people of working age for every retired person, according to the World Economic Forum. Today, the ratio is closer to three-to-one. By 2035, it’s expected to be two-to-one.

Top executives at publicly traded US companies mentioned labor shortages nearly 7,000 times in earnings calls over the last decade, according to an analysis by the Federal Reserve Bank of St. Louis last week.

“A reduction in the share of workers can lead to labor shortages, which may raise the bargaining power of employees and lift wages — all of which is ultimately inflationary,” Simona Paravani-Mellinghoff, managing director at BlackRock, wrote in an analysis last year. “

Is this seriously how normal people think? Improving the bargaining power of workers and increased wages are bad?

“ And while net immigration has helped offset demographic problems facing rich countries in the past, the shrinking population is now a global phenomenon. “This is critical because it implies advanced economies may start to struggle to ‘import’ labour from such places either via migration or sourcing goods,” wrote Paravani-Mellinghoff.

By 2100, only six countries are expected to be having enough children to keep their populations stable: Africa’s Chad, Niger and Somalia, the Pacific islands of Samoa and Tonga, and Tajikistan, according to research published by the Lancet, a medical journal.

BlackRock’s expert advises her clients to invest in inflation-linked bonds, as well as inflation-hedging commodities like energy, industrial metals and agriculture and livestock.

Import labor via migration or sourcing goods? My brother in Christ they are modern day slaves!! I feel like I’m in backwards town reading this what the fuck?!

“ Elon Musk, father of 12 children, has remarked that falling birthrates will lead to “a civilization that ends not with a bang but a whimper, in adult diapers.”

While his words are incendiary, they’re not entirely wrong

P&G and Kimberly-Clark, which together make up more than half of the US diaper market, have seen baby diaper sales decline over the past few years. But adult diapers sales, they say, are a bright spot in their portfolios. “

Oh now the guy with a breeding kink is going to lecture us. Great. /s

“ The AI solution: Some business leaders and technologists see the boom in productivity through artificial intelligence as a potential solution.

“Here are the facts. We are not having enough children, and we have not been having enough children for long enough that there is a demographic crisis, former Google CEO and executive chairman Eric Schmidt said at the Wall Street Journal’s CEO Council Summit in London last year.

“In aggregate, all the demographics say there’s going to be shortage of humans for jobs. Literally too many jobs and not enough people for at least the next 30 years,” Schmidt said.

Oh god not the AI tech bros coming into this shit too. Wasn’t the purpose of improving tech to give people more free time? So they can relax and spend time with family more and actually enjoy life? Isn’t our economy already bloated with useless pencil-pushing number-crunching desk jobs that ultimately don’t serve a purpose?

I’m not going to post the entire article but give it a read. It’s… certainly something. Anyway degrowth is the way of the future.