AI for Precision Agriculture


The industrial revolution of the 19th century streamlined agriculture in previously incomprehensible ways. Mechanized inventions improved efficiency and eliminated much of the labor associated with farming. The technological revolution occurring right now has the potential to change our world just as drastically. 

The introduction of AI into the Precision agriculture landscape has made it possible for farmers to precisely administer inputs to ensure maximum yield and quality. As soil is depleted of nutrients, and water sources become scarce, the necessity of AI tools to track ideal inputs fuels advances in agricultural technology.  

At its most basic level, agriculture relies on 3 specific components; water, a nutrient-dense growing medium, and light. While each component is crucial to crop growth, too much or too little will wreak havoc on crop yield. Farmers are rapidly turning to the predictive ability of AI to protect and enhance crop outcomes. 

Around the world, innovative software tools are gathering data to enhance the accuracy of agricultural AI. Software programs like RedBud compile data that feeds AI’s predictive abilities. As data is gathered over time, AI steps in to suggest solutions to issues before they even arise. 

 There’s no turning back from the benefits that artificial intelligence provides the agricultural community. Moving forward, we can expect both large and small-scale agricultural facilities to continue to utilize AI tools. Those that reject these advances in agricultural technology will find themselves quickly outpaced by their more innovative competitors.

Agricultural AI In Action

In 2019, researchers from Iowa State University built a series of deep neural networks (DNN) that functioned to predict corn yields. Advanced data mining practices allowed them to create DNNs that tracked genotype, environment, and the interactions between the two. The function of the project was to accurately predict crop yield in a way that outperforms the current industry standard of other protocols. 

The results of the project showed that generating AI through well-trained DNNs enhanced crop yield predictability by a significant percentage. The accuracy of the crop yield predictions generated by the Iowa State researchers not only outperformed current methods but also proved that environmental factors weighed more heavily on crop outcome than genotype.

The importance of their findings can not be overstated. AI has proven that genotype plays a less impactful role in crop outcomes than environmental factors. Thanks to these findings, agricultural science can lean away from the idea that genetics is king, and instead focus on creating stronger AI to more accurately predict weather and environmental patterns.

AI and Resource Consumption in Agriculture

One of the hardest barriers to overcome in regards to AI and agriculture is the lack of uniformity in growing conditions. Soil, weather, and pests vary from place to place. This impedes the accuracy of AI’s predictive abilities. To overcome this barrier, massive amounts of data regarding all types of variables are needed.

Several nations in the European Union have begun to combine data gathered from their agricultural AI-based projects. The resulting information is beginning to change the landscape of European agriculture. One of the most successful outcomes is the ability to conserve water with smart-irrigation schedules.

AI gathered data from the EU-led IoF2020 (Internet of Food and Farm) as well as data from the QUHOMA platform.  The combination of these two platforms and AI resulted in a watering schedule that maximized crop yield and minimized waste. The outcome reduced water usage by 6 to 11 percent while preserving crop yield. 



As freshwater sources become less abundant across the world, AI collaborations such as this will be imperative in all aspects of agriculture. To overcome the barrier of growth variables, we don’t just need more data mining, we need bridges between software platforms to train agricultural AI. Programs like RedBud exist to gather data and collaborate with leaders in agricultural science to create those bridges.

Vision-Based AI in Agriculture

Scientists at the University of Florida have crafted an AI-based platform using machine vision to track and monitor crop health, yield prediction, and nutrient needs of crops across the state. The program, Agroview, utilizes drone footage to capture aerial images. AI is introduced that analyzes the images and spots areas of concern.

With the images gathered, the program can generate data on crop outcomes, causes of plant stress, and suggest fertilizer schedules to maximize yield. The aerial imaging also allows Agroview to generate maps that guide farmers in the precise application of fertilizer.

With each image captured, the AI becomes better trained to correctly identify potential problems. As the field of vision-based AI progresses, we can expect AI imaging to reach a point in which it can predict the onset of plant disease or nutrient deficiencies before it becomes a detriment to crop yield.

Impact of AI on Precision Agriculture

Agricultural AI is a young technology. Within the next 5 to 10 years, we can expect the industry to expand rapidly. The convenience of AI is undeniable. The ability to monitor crop health and predict yield remotely eliminates the need for farmers to be ever-present on their land. 

As AI diagnostic tools become stronger, we can expect the need for overapplication of pesticides, herbicides, and fertilizers to be eradicated. Precision agriculture fueled by advances in AI will help farmers allocate resources as they are needed as opposed to preventatively. 


More important than convenience is the necessity of technologies like AI to make farming sustainable. Arable land is finite. Water shortages are becoming the new norm. Extreme weather events negatively impact crop yields. These are facts we all must face as we look to the future. To meet the food needs of the world’s growing population, AI will be integral in improving the efficiency of modern-day agriculture. 

A new agricultural revolution has begun, and AI is the foundation upon which the revolution will be built.

The transformation AI brings to agriculture is undeniable, offering precision, predictability, and sustainable solutions. As the world grapples with ever-changing environmental challenges, the need to harness technology to ensure food security has never been more critical. RedBud stands at the forefront of this change, merging data-driven insights with innovative software tools to propel modern farming into a new age. Don’t be left behind. Discover how RedBud can optimize your farming practices, reduce waste, and predict the unpredictable. Join the agricultural revolution and empower your operations with AI-driven insights today. 🌱

Dive into RedBud’s world of precision agriculture and take your farm’s productivity to new heights