There is a lot of demand on agricultural systems to produce more food because of the world’s population expansion and shifting dietary preferences. There are significant obstacles in maintaining high levels of productivity while satisfying the growing demand for a variety of nutrient-dense crops. AI also helps agriculture manage its resources sustainably.
To optimize water use and irrigation techniques, AI-driven systems may evaluate real-time data on soil moisture levels, meteorological conditions, and plant stress indicators. This guarantees effective water distribution, lowers water waste, and encourages environmentally friendly agricultural methods. The use of artificial intelligence (AI) to agriculture holds promise for revolutionizing food systems and assisting in the resolution of the world food crisis.
AI can assist farmers in making data-driven decisions, optimizing resource use, and minimizing environmental effects by evaluating data from several sources. AI integration in agriculture, for instance, has been shown to reduce water use by 50% and pesticide use by 60%, according to World Economic Forum research.
A $65 billion potential might result from improving 15 agricultural datasets in India, a nation home to one of the most well-known Agtech businesses. These datasets include soil health records, crop yields, weather, remote sensing, warehousing, land records, farm markets, and insect photos. From forecasting crop yields to enhancing soil health, this article will examine the applications of AI in agriculture and how it may help create a more sustainable and food-secure future.
Comprehending the Issue of Global Food Security
Several factors, such as food availability, accessibility, use, and stability, are all part of the vast and intricate concept of food security. The state in which each person has the physical, social, and financial resources to acquire a sufficient amount of food that is safe and nourishing, in compliance with their dietary needs and preferences, is also included. The goal of agricultural development programs started by various nations and international organizations is to increase agricultural output, support rural livelihoods, and promote the use of sustainable farming practices.
The four primary aspects of food security are:
1. Physical Availability of food: This refers to the “supply side” of food security and is based on net trade, stock levels, and food production levels.
2. Access: Food security at the household level is not ensured by a sufficient national or international food supply. To achieve food security goals, policymakers are now more focused on incomes, spending, markets, and pricing as a result of worries about inadequate food access.
3. Food usage: Generally speaking, usage refers to how the body maximizes the different nutrients found in food. People who follow proper care and feeding procedures, prepare their food, and eat a variety of foods will consume enough energy and nutrients.
4. Long-term stability of the remaining three dimensions: Having insufficient access to food regularly puts your nutritional status at danger, thus even if you eat enough food today, you are still deemed to be food insecure. Your level of food security may be impacted by unfavorable weather, unstable political environments, or economic situations (such as joblessness or growing food costs).
Advantages of AI in Agriculture
1. Financial Savings
AI in agriculture can be expensive to install initially, but there is no denying the long-term benefits through productivity, efficiency, and analytical roadmaps. By automating processes like yield forecasting and crop monitoring, AI in agriculture lowers labor expenses. Farmers may estimate future yields using past data and weather trends thanks to AI-driven predictive analytics. This makes it possible to organize planting and harvesting times more efficiently, which eventually raises total profitability and production.
2. Data-Driven Agriculture: Accuracy and Productivity
By analyzing enormous volumes of data on weather patterns, soil conditions, and past crop yields, artificial intelligence (AI) in agriculture makes data-driven decisions possible. Through the identification of variables that impact crop yields, such as temperature or moisture content, farmers may make better decisions regarding fertilization, irrigation, and seeding.
AI frees up farmers’ time for other pursuits by automating duties like crop monitoring. Farmers can collect and process data more effectively with AI-powered predictive analytics, which helps them make better decisions at every step of the farming process, from preparing for harvest to evaluating the health of the soil.
3. Resilience and sustainability
AI in agriculture improves environmental stewardship and maximizes resource use, which promotes sustainability and resilience. Farmers can monitor chemical, water, and energy inputs with AI-powered apps, allowing them to make adjustments for more environmentally friendly methods. AI also monitors soil health parameters including pH, moisture, and nutrients, which helps guide fertilization and irrigation choices for better soil health.
4. Early identification of Pests and Diseases
Early disease and pest detection is made possible by AI in agriculture, which is a vital tool for preventing crop damage and yield loss. AI examines crop photos for indications of pests or diseases using image recognition technology, empowering farmers to take preventative action. An extensive database of plant illnesses is provided by AI programs such as Plantix, which also give insights and solutions for efficient disease control.
Challenges of AI in agriculture
AI implementation in agriculture may also provide several difficulties, including:
1. Problems with integration:
It can be challenging to integrate AI with current farm machinery and systems. New AI technologies might not work with outdated equipment. To get the most out of AI, farmers might need to repair or enhance their equipment.
2. Security and privacy of data:
It is crucial to make sure that agricultural data is collected, stored, and shared securely. Sensitive data regarding their operations and yields must be protected by farmers. Strong cybersecurity defenses and data management procedures are needed for this. Furthermore, it is difficult to handle problems about farm data ownership and use. Who has control over the data produced by AI systems is a question. Farmers and technology providers must have explicit agreements.
3. Cost of Agriculture’s Transition to AI:
The farming industry is one where the high cost of change is very evident. Compared to several other industries that embraced artificial intelligence enthusiastically, this cost structure is significantly different. In a farming system, the risk element is on a different scale; heeding the incorrect advice could result in a farming season’s worth of lost crop yield. Adopting modifications on a smaller size, even if the farmer farms on a larger scale, is one way to address this issue, which is already practiced in farming. tiny scale refers to a tiny area of a farm or minor adjustments made over several growing seasons while considering input.
4. Data Gathering
Even though precision agriculture has grown over the last three decades, two major issues have prevented machine learning predictions from being widely used in the industry. First, a sizable percentage of farmers and growers have not embraced precision agriculture; there are numerous studies and explanations for this. Second, using IoT or other channels to get all the necessary data for prediction is not the same as using precision-Ag technologies.
Five AI-related agricultural applications You Should Be Aware of
1. Robots That Are Ripe
Leading the way in agricultural innovation, Ripe Robotics builds robotic apple and stone fruit harvesters with the goal of complete automation. Their robot, “Eve,” carefully gathers apples, plums, peaches, and nectarines in Shepparton, Australia, utilizing big data and artificial intelligence to maximize operational efficiency and evaluate fruit quality in real time.
2. Solutions for Yield Technology
An Australian ag-tech startup called Yield Technology Solutions is transforming agriculture with scalable digital solutions by utilizing IoT and AI. Their Sensing+ microclimate system helps producers make informed decisions by providing personalized information and forecasts through intuitive apps. The Yield improves commercial outcomes by providing precise yield projections using AI models powered by aggregated microclimate data.
3. Solutions from AquaTerra
AquaTerra Solutions analyzes soil data gathered by their sensing technology and Internet of Things platform using artificial intelligence. Farmers may make data-driven decisions for increased farming operations’ productivity and efficiency by using this AI-powered analytics interface, which gives them actionable insights on fertilizer application and irrigation management.
4. The ARC Research Center
The Australian Government and business partners sponsor the ARC Research Hub for Driving Farming Productivity and Disease Prevention, which focuses on incorporating AI technologies into agriculture. Australia’s competitive edge in the worldwide market is strengthened by the Hub, which is hosted by Griffith University and works with top universities and research organizations to develop cutting-edge AI solutions that improve farming efficiency, lower costs, and reduce disease risks.
5. Bitwise Farming
By using computer vision and machine learning to precisely count and measure horticultural crop components like berry and bunch numbers, Bitwise Agronomy’s Greenview product uses artificial intelligence (AI) to help in yield estimation. This cutting-edge system uses off-the-shelf cameras to provide exact data for operational decisions, addressing issues like erroneous yield estimations and giving a cost-effective solution.
Prospects for the Future
Artificial intelligence is developing quickly, opening up new possibilities and breakthroughs in the sphere of food security. As technology advances, several new developments and opportunities are influencing how AI is being used to address issues related to global food security. A Market market analysis projects that AI in the agriculture market will develop rapidly, from $2.35 billion in 2020 to $10.83 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 35.6% during the course of the forecast year.
To help farmers better plan their operations and capitalize on the ideal planting season, AI will also continue to help with weather forecasting. Even while AI has many advantages for agriculture, most farmers around the world—especially smallholder farmers—do not have the resources to put these technologies into practice. Because they often lack access to technical knowledge, smallholder farmers find it challenging to properly use AI systems. Additionally, many lack the funds necessary to buy the machinery and software needed for AI-based farming.
In Conclusion
The challenge of providing food security for a growing world population, coupled with climate change and finite resources, calls for innovative and transformative solutions. The potential of artificial intelligence as a promising tool to address these issues and enhance global food security has been investigated in this study. It is easier to optimize inventory control, traceability, and quality control procedures when artificial intelligence is used in supply chain management and logistics systems. Food loss is effectively reduced by this deployment, which also ensures that products are delivered on time.
FAQ’S
1. How might AI aid in the fight against world hunger?
By streamlining food supply chains, increasing crop yields through precision farming, and forecasting climatic impacts to lower the risk of food scarcity, artificial intelligence (AI) helps fight world hunger.
2. How does artificial intelligence contribute to sustainable agriculture?
By facilitating resource-efficient farming, tracking soil health, and cutting waste through sophisticated data analytics and IoT integration, artificial intelligence (AI) enhances sustainable agriculture.
3. Can AI help cut down on waste in agriculture?
By evaluating production trends, predicting demand, and optimizing logistics to align supply with market demands, artificial intelligence (AI) lowers agricultural waste.
4. How might AI help farmers in underdeveloped nations?
Through mobile technology, AI helps farmers in poor nations by giving them access to weather forecasts, tools for detecting crop diseases, and market pricing trends.
