AI in Agriculture: Leveraging Technology for Sustainable Farming
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Sprawling fields and farms feed our world, and agriculture is the age-old industry that sustains humanity. Yet, a pivotal moment is underway. We are grappling with the growing population, global warming, and resource limitations. How can agriculture survive and thrive in such a challenging landscape?
It seems that traditional agricultural practices are struggling to keep up with the demands of this century. Climate changes, pests and diseases, limited water resources, and arable land growing scarcer day by day call for innovative solutions. Looking for solutions, agricultural experts find more benefits of AI/ML than any other industry.
Let’s look at some stats
on AI in agriculture
to use AI
$4.7B by 2028 at 22.55% CAGR
in the US
Microsoft, IBM, Granular Inc., aWhere Inc., are the leaders of AI agriculture market
What is AI in agriculture?
Artificial intelligence in agriculture refers to the use of artificial intelligence and related technologies in various aspects of farming and agricultural activities. It involves the application of machine learning algorithms, data analysis, and automation with the aim of improving productivity and sustainability in agriculture. The key areas where AI is making an impact include crop monitoring and management, weed and pest management, soil condition control, and precision agriculture.
Artificial Intelligence (AI), machine learning (ML), and Cognitive computing are essential technologies in the field of manufacturing. AI empowers computers to think like humans, enabling reasoning, problem-solving, natural language understanding, pattern recognition, and predictions. ML, a subset of AI, focuses on algorithms that enable computers to learn from data and improve performance over time, making independent decisions based on examples.
Cognitive computing, another AI subset, simulates human thought processes, creating systems that learn, reason, understand, and interact with humans naturally. They use context-aware natural language processing for effective communication. These technologies collectively aim to enhance computer intelligence and human-computer interaction. AI has found applications across various industries including manufacturing, healthcare, and agriculture.
Applications of AI in agriculture
The application of artificial intelligence in agriculture is promising, since farmers see lots of benefits of integrating all farm equipment, analyzing data, and making data-driven decisions. In the focus of every farmer considering AI solutions for their businesses, is the specific use case and the outcomes. So, let’s take a closer look at the use of AI in agriculture.
Soil monitoring refers to the collection of data on soil conditions, properties, and health. It involves the use of various techniques to assess the attributes of soil. The attention to soil monitoring in agriculture rose with the increase in food demand. The global population is about to reach 9.7 billion by 2050, while land and energy, water supplies are tight. By 2030 the global water supply deficit will reach 40%. It leaves farmers grappling with escalating expenses, and limited resources. So, what can improve this situation? Nutrient management, sustainability, crop health, and optimization of resource utilization are the areas of focus for farmers. Soil monitoring covers most of these points, hence countries all over the world are investing in the research of possible solutions.
The Berlin-based agri-tech startup PEAT developed a deep-learning application that helps farmers address defects and nutrient deficiencies in the soil. It utilizes image recognition technology and allows users to gain valuable insights into the condition of the soil with their smartphone camera. Hence, Plantix is an app that helps farmers stick to their sustainability goals, and minimize the waste of resources.
Agricultural irrigation constitutes 70% of total water consumption. It is also increasingly vulnerable to water-related challenges. In recent years, places like China and the United States depleted their resources. In addition, climate changes are about to bring more extreme weather events, such as floods and storms that will exacerbate the water crisis even more. The forecasts also say that water quality is declining due to pollution and salination from the rising sea levels. Since the agricultural sector is heavily reliant on water, stocks, and crops are under threat. Agriculture holds a central position in addressing the water crisis since it is one of the largest consumers and pollutants.
To minimize water utilization and expenses related, farmers need to identify overwatered and underwatered areas. One of the daily tasks for farmers is assessing the amount of water needed for good yield. For example, with the cotton flowers, overwatering causes more leaves to grow instead of the flowers. One of the metrics used to check the irrigation of the crops is evapotranspiration. It shows the sum of evaporation from the soil and transpiration from the plants. Satellite imagery assists with that but the real progress has been made with the introduction of the Internet of Things (IoT). Sensors managed to give more insights into the plants’ lifecycle and help farmers understand how to optimize their crop care. They also gave rise to connected farming. Now, a new tool comes to the market and it is represented by AI. Powerful computer vision and data analysis capabilities enable agronomists to get more accurate information for decision-making. Imagery combined with the sensor data, processed by AI-powered tools decreases waste agriculture dramatically.
Unexpected equipment breakdowns, especially for smaller farms pose a severe threat. It impacts financial flow and causes a loss of productivity. As an option, farmers try to increase the frequency of scheduled maintenance but predictive one emerges as a more cost-efficient solution. To introduce predictive maintenance, an organization needs to leverage data-analyzing tools and sensors that detect faults before they happen. For example, among the threats that constantly chase equipment is corrosion. Agricultural equipment is exposed to sunlight, different weather conditions, and fertilizers for extended periods. Meanwhile, it remains unused for weeks in winter. To prevent corrosion IoT sensors, and GPS/activity tracking can be combined with the AI models. To be specific, a model can be created based on the data collected by the mentioned means. As a result, it reduces routine maintenance frequency, cuts overall maintenance costs, minimizes unplanned downtime, and decreases repair costs.
Image analysis for weeds and pests
The battle against weeds in the realm of modern agriculture is an imperative component of ensuring healthy crop yields and mitigating economic losses. Weeds are those unwanted plants that compete with crops for essential nutrients, resources, and sunlight, and cause staggering economic losses of 40 – 50%.
The integration of cutting-edge technologies has become essential in addressing these challenges and fostering sustainable farming practices.
Image analysis, video processing, machine vision, and artificial intelligence have emerged as valuable tools in the farmer’s kit. These technologies allow accurate assessments of crop nitrogen levels, real-time monitoring of crop growth, and early identification of plant diseases and weeds.
Livestock monitoring promotes animal welfare, prevents diseases, and supports sustainable farming practices. It reduces risks and optimizes resources, as well as helps farmers make informed decisions. By applying effective remote monitoring techniques, farmers get the following benefits:
- Nutritional management
- Resource management
- Behavioral insights
- Disease prevention and control
- Health and welfare
While agriculture experts already have multiple ways of conducting livestock monitoring, AI is here to revolutionize their accuracy and efficiency.
For example, computer vision and machine learning algorithms are used for the following:
Health monitoring. By utilizing an IoT technology stack, farmers collect data about body temperature, illness, injuries, or even stress. AI algorithms can be applied to analyze the information and alert in case an animal is sick.
Behavior monitoring. Using the footage from cameras AI can detect limps or appetite changes that signal health problems.
Predictive analytics. Analyzing historical data with AI algorithms helps predict future trends in animal behavior as well as health. Farmers use this information for decision-making on nutrition and breeding.
Optimizing post-harvest produce sorting
In addition to monitoring crops, AI algorithms are capable of making the labor-intensive sorting stage easier and cost-effective. Through computer vision, it can categorize produce based on size, shape, and color. In contrast with manual sorting, the time spent on it reduces without sacrificing the quality.
Several AI-powered solutions for sorting have already been introduced to the market. Among them is GREEFA which specializes in fruit sorting and packaging. The AI-powered machines assess fruits for quality, size, color, and defects to ensure consistency in packaging.
Yield mapping is a technique that farmers use to create detailed maps or visual representations of crop yields within a farming area. It involves the collection and analysis of data. The maps provide insights into the variability of crop production and give an understanding of which areas perform better or need additional attention. Several technologies can be combined for yield mapping and among them are machine learning algorithms that analyze data on soil color to predict soil yields for a certain crop. The data is usually gathered by drones, GPS systems, and sensors. Some other applications of AI/ML in yield mapping include:
- Data collection and integration. AI/ML automates the collection and integration of data from several sources, like GPS, sensors, and drones, and ensures that the mapping process is accurate and precise.
- Data processing. Since the volumes of data gathered by farmers are large, AI/ML can be implemented to process it quickly and accurately. The algorithms can identify patterns and correlations between them.
- Image recognition. With the analysis of images, farmers identify variations in crop health, which can be related to yield variations.
- Variable rate technology (VRT). AI/ML is used in VRT to adjust irrigation, and fertilization rates in real time based on the variability detected in yield maps. This way farmers optimize the use of resources.
How can agriculture businesses prepare for AI adoption?
To assist you in formulating your AI adoption strategy when you’re uncertain about where to begin, here’s a set of inquiries that can provide you with clarity and guidance:
- What are your primary aims and objectives for incorporating AI technology into your business operations?
- Are there any existing manual processes that AI has the potential to replace?
- Can AI be employed to validate and enhance the quality of processes, as well as reduce defects?
- Do you have access to well-structured, high-quality, and pertinent data suitable for AI training?
- Have you identified potential sources of data and potential challenges related to data integration?
- Does your team possess expertise in AI, including data scientists and machine learning engineers?
- Is your current IT team adequately equipped to support AI implementation?
Integration with Existing Systems:
- Have you conducted an evaluation of how AI solutions will align with your present IT infrastructure and software?
- Are there any anticipated compatibility or integration issues that need to be addressed?
Scalability and Future Expansion:
- Have you considered the scalability of your AI solutions in relation to your business’s future growth?
- Is your existing infrastructure capable of accommodating increased AI workloads and demands as your business expands?
Leverage Euristiq expertise to implement your AI strategy successfully
With prior experience in precision agriculture and smart farming initiatives, the Euristiq team boasts AI experts ready to assist agricultural businesses in crafting and executing their AI strategies. Leveraging our extensive knowledge in the Internet of Things, cloud technologies, and legacy modernization to ensure the seamless integration of AI tools and achieve optimal operational efficiency.
What does AI stand for in agriculture?
How is AI used in agriculture?
How is AI used in smart farming?
What are some examples of AI in farming?