Harvesting Data: How AI Boosts Small Farm Productivity?

   How autonomous equipment improve crops with artificial intelligence?

 

In nearly five decades, the world’s population has surged, reaching the 8 billion mark. With the current trajectory, we’re on course to welcome the 9 billionth human within the next 15 years. This raises a critical question: how do we ensure adequate food supply for everyone?

The term “artificial intelligence” (AI), coined by John McCarthy in 1955, has evolved to play a pivotal role in addressing the challenges of our burgeoning food demands.

Maintaining a healthy lifestyle typically involves a daily intake of 2000 calories. However, the task of feeding an ever-growing population is daunting, especially as climate change puts additional pressure on agricultural production and water availability.

 Today’s farmers must possess a deep understanding of various agricultural aspects, from fertilizers and soil to crop-specific insecticides, planting, irrigation, and the effects of weather. They face the constant threat of pests, which can destroy up to 40% of global crops annually, and must strive to increase food output while conserving energy and water.

 The shift away from farming due to urbanization, immigration challenges, and generational changes has led to a persistent labor shortage in agriculture, necessitating a move towards less labor-dependent farming methods. In this context, technology, particularly AI, has become indispensable for successful crop cycles.

The autonomous farming

The autonomous farming sector is experiencing rapid growth, with around 200 AI-centric agricultural startups in the U.S. alone. AI’s role in agriculture includes innovations like self-driving tractors, robotic crop inspectors, and autonomous sprayers. Indoor farming operations such as Plenty and AppHarvest are harnessing AI and computer vision to monitor crops and tailor the growing conditions for enhanced nutrition and taste, while also employing robots for harvesting. Blue River Technology utilizes AI for precise weed control, minimizing the need for manual labor.

The challenge extends beyond cultivation and harvesting. The United Nations reports that approximately 17% of global food production is wasted, which represents a significant portion of the total energy consumed by the food system. This waste not only squanders vital resources like water, land, energy, labor, and capital but also contributes to greenhouse gas emissions when disposed of in landfills, further impacting climate change.

Leveraging AI for Agricultural Advancement Agricultural leaders can leverage AI to boost productivity and reduce waste, even as costs continue to rise. Here are some strategies to optimize AI in agriculture:

• Embrace a variety of data sources: Employ a range of technologies to collect data, such as videos, IoT sensors, and computer vision, which capture diverse inputs like images and light.

• Monitor extensive data points: A single plant can generate millions of data points related to the effects of light, water, weather, and environmental changes on production, flavor, nutrition, disease, and waste. This wealth of data can yield insights over time, leading to efficiency improvements in yield, waste reduction, nutritional enhancement, and the conservation of scarce resources like water and arable land.

• Implement continuous monitoring and AI adaptation: Persistent observation and AI evolution can enhance all aspects of production, operations, and distribution. Drones, for example, can provide imagery and help train computer vision models, offering ongoing insights into crop growth and environmental conditions. AI enables farmers to closely monitor their crops and make timely adjustments to challenges such as extreme weather events, by modifying water usage or deploying protective measures.

Integration of AI in Agriculture

Adopting these methods can significantly improve agricultural productivity and minimize waste, thereby contributing to global food security and environmental sustainability.

The integration of AI in combined operations exemplifies the technological advancements in agriculture. Traditionally, operators manually adjusted the combine’s sieves when they spotted excess cob or foreign material in the grain. Now, AI leverages image databases to distinguish between high and low-quality grain, enabling automated sensors to adjust the combine’s settings, such as closing a lower sieve or modifying the fan or rotor speed for optimal harvesting.

• Industry experts like Gartner predict that organizations will increasingly adopt AI to refine their decision-making capabilities. Those who embrace AI swiftly will gain a competitive edge, becoming more adaptable and responsive to changes within their ecosystems.

The Impact of AI on Global Food Supply and Waste Reduction

Reflecting on John McCarthy’s 1959 observation about computers solving complex problems, it’s evident that his insights remain relevant. With the global population now at 8 billion, we continue to rely on technology, particularly AI, to meet our food supply needs efficiently and reduce waste, just as McCarthy envisioned the potential of intelligent machines. 

The quest for fully autonomous construction vehicles remains a challenging yet transformative aspiration. A few years back, the vision of automating vehicles to eliminate traffic fatalities and using robotic machinery to address housing and infrastructure deficits seemed within reach.

Autonomous Construction Machinery

In 2017, Built Robotics embarked on a mission to enhance construction sites with autonomous excavators. CEO Noah Ready-Campbell envisioned a future where such equipment would be more prevalent on construction sites than autonomous cars on highways. However, after years of trenching autonomously, Built Robotics pivoted towards solar farm installations, introducing the RPD-35, a robotic pile driver designed for a specific task—driving steel beams into the ground.

Ready-Campbell now sees solar energy as the focal point for the company’s future, aligning with the US’s infrastructure and climate-change initiatives. This shift reflects a broader trend where the reality of AI in construction has not quite matched the initial promise.

Globally recognized firms like Caterpillar, Doosan, and Volvo have experimented with autonomous construction machinery, but widespread adoption remains elusive. The dynamic and complex nature of construction sites presents unique challenges for AI and robotics, contrasting with the more predictable environments of public roads.

Caterpillar's Journey in Automation

Caterpillar, a leader in construction equipment with a history of AI innovation, has successfully deployed autonomous trucks in mining operations but has yet to commercialize automated construction machinery. Despite ambitions to expand software sales for autonomous machinery control, the company continues to refine its technology in partnership with select clients, ensuring safety and reliability before full-scale deployment.

The journey towards autonomous construction machinery is ongoing, with industry 

leaders cautiously advancing towards a future where machines can safely and effectively contribute to construction projects, transforming the industry and potentially fulfilling the long-held dream of full automation.

Caterpillar’s journey into automation began over a decade ago, targeting both mining and construction. However, the mining sector saw quicker advancements due to factors like semi-permanent roads and the ability to secure underground areas. Remote locations of mines also made automation a more viable solution than construction sites' transient and ever-changing nature.

Challenges in Adopting Autonomous Construction Equipment

The path to fully autonomous construction equipment is believed to involve an interim phase of semi-automation, where equipment is remotely operated. This approach allows operators to control machinery from anywhere, akin to playing a video game, potentially even from the comfort of their homes. Meanwhile, AI specialists work on identifying tasks that can be automated.

Predictions for the Near Future

Currently, heavy machinery operators have access to limited automation features, such as automatic grading with dozers. The ultimate aim, as stated by Caterpillar’s chief engineer Michael Murphy, is to enable a single individual to oversee multiple machines simultaneously, with algorithms handling the bulk of the tasks.

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While Caterpillar’s automated machinery still looks like traditional equipment, companies like Volvo and Doosan are designing cabin-less machines, anticipating a future where no human operator is needed on board. Volvo has already deployed a cabin-less hauler in a Swiss quarry and autonomous trucks in a Norwegian mine, but autonomous construction machinery is not yet operational on construction sites.

   Built Robotics' Shift in Focus 

Anthony Levandowski, CEO of Pronto.ai, acknowledges the striking appearance of cabin-less excavators but believes their widespread adoption is still a distant reality. Levandowski, once an advocate for self-driving cars, now sees autonomous vehicles as lagging behind expectations. Pronto.ai, similar to Caterpillar, is focusing on automating trucks for mines and quarries, where the controlled environment simplifies operations compared to public roads.

Levandowski predicts only modest advancements in construction automation in the near term, with tasks like automatic grading and dust suppression being the focus. Meanwhile, Built Robotics’ CEO Noah Ready-Campbell has shifted the company’s R&D efforts to a robotic pile driver, despite previous successes in automating various construction machinery. The challenge lies in convincing customers of the value of automation, as significant pain points must be addressed to encourage adoption and behavioral change.

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