Smart Farming: How AI and Renewables Are Revolutionizing Harvest

Agricultural stakeholders have a pressing choice to make — embrace emerging technologies or not. This decade’s focal players include renewable energy generators and artificial intelligence (AI). The two synergize, so those who adopt both early will have the upper hand when optimizing and predicting harvest yields. Decision-makers should be aware of these applications to crystallize how renewables and AI benefit farming operations.

KEY INSIGHTS

1/ The integration of artificial intelligence and renewable energy technologies allows farmers to predict and optimize harvest yields more effectively. They will be able to optimize resource use in farming operations including regulating irrigation, automating seeding, and pesticide application. 2/ Early adopters of both technologies will have a significant advantage in agricultural efficiency and sustainability. Nations will be able to better improve food security. 3/ Perimeter-Area Soil Carbon Index (PASCI) is a critical AI-driven tool that assesses soil carbon content, helping farmers improve soil health and crop productivity.

Perimeter-Area Soil Carbon Index (PASCI)

Abusive farming methods have sapped the soil of its nutrients. Many farms struggle to treat their soil and bring its components to reasonable levels. PASCI is an agricultural measurement system that identifies carbon content. 

Soil Health Analyses

AI is critical for accurate PASCI assessments because it contextualizes soil health and land fertility against other performance metrics and previous years of operation. It can also create visualizations and maps so farmers know which crop regions to give the most attention to. AI-informed PASCI is crucial for understanding the crops’ carbon footprint. The plot’s ability to sequester carbon correlates with how its growth cycles will unfold. 

Carbon Sequestration and Farming Practices

PASCI also informs farmers on how to increase sequestration opportunities based on metrics and suggestions. For example, results could encourage cover cropping, agrivoltaics or adopting a no-tillage routine. 

Seeing and applying the knowledge from one metrics analysis could catalyze other tests leveraging AI, such as pH, and target even more practices to perfect yields.

Eric Ariel Salas, a geospatial research scientist at Central State University, considered the versatility of PASCI. He has been extensively using AI and machine learning in his research to develop algorithms that improve crop productivity:

“One of the key achievements from our lab at Central State University in Ohio is the development of the Perimeter-Area Soil Carbon Index (PASCI). This index helps predict and map soil organic carbon (SOC), which is the most critical indicator of soil health and a major determinant of long-term crop productivity. What makes PASCI especially powerful is its ability to work with open-access airborne and satellite images, including hyperspectral and multispectral data from NASA’s Landsat and the European Sentinel-2 satellites. The development of PASCI allows us to remotely quantify, predict and map SOC, providing a vital tool for sustainable agriculture.”

Agricultural Yield Forecasts

Numerous factors determine a yield’s success, including weather and soil health. AI integrations powered by renewable energy could work around the clock to collect data. With enough training, AI models with machine learning algorithms could have predictive accuracies of 50%-99%.

Weather Considerations

Yield predictions are more accurate when more information is gathered. Changing weather conditions are behind 90% of crop damage, so preventing this monumental deterrent could be the deciding influence for a lucrative season. 

AI and renewable energy resources boost a farm’s resilience through soil reinforcement or by producing fewer contaminants or pollutants. Solar panels have proven to increase biodiversity, regulating habitats to make lands less water-stressed and more symbiotic with local species. Deploying these technologies resourcefully could make a farm better withstand difficult weather conditions, making harvests more consistent.

Optimized Schedules

Yield forecasts also promote better decision-making among farmers. They will know what plants are likely to succeed, provide the most revenue given market trends, and plan the best time to harvest. AI directs farmers to the most high-value efforts instead of wasting their time and resources on plants that don’t grow or remain profitable. Because AI can scrub the internet and other databases for agricultural data worldwide, farmers can learn how similar competitors operate. 

When are they harvesting a specific plant, and did the year’s temperature shifts cause them to pick late or early? How did it impact when they went to sale, and what were the crop’s prices? Instead of scrambling to find answers to these queries, an AI model could generate suggestions for agriculturalists. 

Eventually, farmers will become more intuitive because of information exposure. One study demonstrated how effective AI was for predicting harvest times on an olive farm. After three years of development, it became 90% accurate in what the researchers claimed to be one of the most complex problems in precision agriculture.

Renewable Impacts

Renewable energy is another key player in yield forecasting, especially when combined with predicting weather. For example, if a farm uses solar or wind power to cut utility bills and power machinery, then fluctuations could impact the crop’s health and productivity. 

Smart renewable assets combined with AI optimize resources and labor to plan with the market and nature in mind. They become the default resources for synthesized information instead of having knowledge silos across employees. Everything is compiled in technological hubs. Additionally, AI modeling can show how much green energy generators impact growth compared to fossil fuel-reliant competitors.

Thanh-Long Huynh is the CEO and co-founder of QuantCube Technology. He suggests forecasts will have global impacts on food availability:

“With increasing anxiety around food security and global supply chains, commodity traders and governments urgently need a solution that delivers more dynamic and accurate insights into the outlook for agricultural harvests. The QuantCube Agricultural Yield Forecasts and sentiment indicators use AI technology and satellite data to accurately estimate crop production at a regional, national, and global level for corn, soybean, wheat, and rice crops. As a result, countries can diversify imports and enhance food security, while investors can adjust their soft commodities strategies and get ahead of the market in advance of the publication of infrequent official data.”

AI-Based Precision Agriculture

Precision agriculture leverages equipment like the Internet of Things (IoT) and drones to pool as much data as sensors can. Combine this with AI, and it becomes even more powerful in guessing how well growth cycles will unravel. Only 12% of farms use these advanced technologies when they have been proven to provide benefits exceeding the value of the initial investment.

Data Transparency and Preventive Action

Data visibility is critical for noticing the smallest changes that could snowball into something catastrophic. Catching anomalies early is a pillar of modern harvest predictions and analysis because it allows proactive response. 

For example, if sensors and cameras atop agrivoltaic solar panels spot leaves changing colors, farmers could catch a blight or invasive species faster than those without these resources. Workers will no longer feel oblivious to gradual changes in crop yields.

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Resource Efficiency

Monitoring crop health with accuracy also ties into resource efficiency. Precision agriculture spots nutrient deficiencies, overwatering, and sunlight exposure. Renewable energy resources could power irrigation systems to automatically distribute water in the right quantities if the land is too dry. Programmed drones can fly on schedules to distribute seeds or pesticides to ensure nothing influences output and there are no gaps in planting schedules. All this enables farmers to transform their fields into a no-waste zone.

Leaficient’s CEO, Andy Rape, made AI the company’s focus to engage in climate change mitigation. According to him, Leaficient is focused on helping farmers adopt new methods of farming in response to climate change: 

“We do this by using AI to help farmers see the health of their crop in real time. Our technology improves farming productivity, which is vital as NASA predicts that without intervention, the productivity of farming could decrease up to 30% by 2030 as a consequence of climate change.”

Predicting Agriculture

These technologies are only three examples of what agricultural stakeholders could invest in to strengthen their companies. Staff will enhance their digital literacy and become more knowledgeable about their land because of data visibility. 

Additionally, there will be fewer losses because of increased data accuracy. It expands food access worldwide in an age where scarcity is rampant, so the environmental implications are worth the financial investment and technological learning curve.