From Farm to Cloud: How AI is Transforming Agriculture Industry through Cloud-based Solutions

The world population is projected to be 10 million by 2050 according to the UN. We need to increase food production by 60% to feed the additional 2 billion people and have only 4% more cultivatable land available to us. The agriculture industry contributes to global warming as it is responsible for 12% of global greenhouse gas emissions such as methane and nitrous oxide. Moreover, according to FAO, 70% of the world’s water is used for agriculture. This creates an urgency in developing sustainable solutions to problems faced by the agriculture industry.

Climate change, water shortages, and heat waves are a great challenge to the steady growth of the agriculture industry. This calls for timely action and rapid transformation in the agriculture industry as it has a lot of challenges to overcome in a short time. Agriculture has remained the least digitized sector despite the radical transformation in several industries over the years. Advanced technologies like AI and cloud are key for agriculture digital transformation and can help farmers meet their pressing productivity needs with sustainable practices. Let’s explore how.

Challenges in AI and cloud adoption in Agriculture

Despite advances in technology, there are several challenges when it comes to the adoption and implementation of AI and cloud in agriculture which hinders the digitization of agriculture.

Lack of awareness

The farmers who are the producers have the least awareness about the technology and its benefits. Illiteracy, lack of experience using technology and communication gap also contributes to the problem. Farmers need training and awareness in using technology and AI-enabled solutions and require increased accessibility to these through additional features like local language capabilities.

Security and privacy concerns

Implementation of AI and cloud-based solutions requires various data and the internet which can cause the risk of data leaks and cyberattacks. The lack of clear regulations and policies in place for using AI can also raise legal issues.

Lack of trust

Farmers might have a resistance to adoption of technology due to the lack of trust towards the service providers and as there is no physical contact. Many services will be maintained by third-party service providers which can also raise the question of data security.

Lack of infrastructure

Mechanisation of agriculture is not widely adopted in developing countries. The affordability can be a question. Supporting infrastructure like high-speed internet is important for AI and cloud-based solutions.

How AI and cloud transform agriculture

Getting food to the plate involves various steps and processes and is dependent on variables like soil, water, and climate conditions. Technologies like AI, cloud, and IoT can play a major role in increasing productivity and ensuring food security with assistive technologies in every stage of the agri value chain.

Predictive analytics

Predictive analytics in agriculture use statistical methods to analyze current and historical data from multiple sources to make predictions about future farm outcomes and provide actionable insights. This helps farmers optimize resource use, improve performance, predict market conditions, and plan for production and challenges. Some of the ways in which predictive analytics help agriculture is:

• AI and ML predictive analytics tools can help to make decisions like time of sowing, irrigation, and using fertilizers, and provide weather forecasts and recommendations based on data.
• AI systems that use satellite images can use real-time data with historical data to predict and alert farmers on seasonal insect attacks, and pest attacks and assist in fighting against pests.
• Crop yield mapping uses supervised learning algorithms to find patterns in large data sets such as sensor data, social data, and drone data to make yield predictions and price forecasting.
• Predictive analytics can also help in reducing the detrimental effects of farming on the environment and reduce the environmental footprint of farmers.

Agricultural robotics

The agricultural sector is having an increasing shortage of workers and robotics can fill the gap between manpower and production needs. Advanced technology options like smart robots, agribots, and robotics can take up many tasks like precision seeding, distributing fertilizers, weeding, and harvesting. Some of the common use cases of robotics in agriculture are:

• Autonomous agriculture robots can multi-task and help in the end-to-end automation of farming tasks from soil analysis and environment monitoring to seeding, planting, weed control, and harvesting.
• Intelligent irrigation robots can help reduce the wastage of water used in agriculture by using need-based irrigation and precision irrigation.
• Intelligent spraying using robots can also help in spraying herbicides and fertilizers efficiently in the field and reduce costs.

Monitoring and surveillance

Machine learning algorithms can analyze massive data sets such as soil data, moisture, weather data, etc, and can provide advice for sowing, fertilizer usage, and water requirements to optimize productivity and maximize yield. This allows real-time collection and analysis of data from the field and to gain insights to make data-driven decisions and timely actions. Some of the advanced AI-ML and cloud-based technologies used in monitoring agriculture are:

• Ariel imaging with drones and smart sensors can be used to capture real-time data from the fields to monitor crop health, moisture, and natural nutrient levels in the soil, identify pests and rodents, and take timely actions. For example, in the case of pest control, AI-ML algorithms can identify the most affected areas and help use the optimal mix of pesticides to reduce pest threats from spreading to healthy areas.
• Mobile phone-based applications that use ML algorithms to identify diseases, nutrition deficiencies, and pest attacks by analyzing photos of crops and soil and provide tips like soil restoration techniques, fertilizers to use, etc.
• ML-based video surveillance systems can be trained to detect animal or human breaches in fields and help farmers to secure farm perimeters, especially in remote facilities.
• Many farmers relying on livestock can also use AI and machine learning to monitor and analyze vital signs, food intake, ailments ,and activities to keep them healthy and increase milk production.

Nuvento boosting the agri-value chain with smart solutions

The agriculture sector is in demand of rapid technological transformation from farm to fork. Nuvento has been at the forefront of creating AI and cloud-based solutions that assist farmers and the agriculture sector. Nuvento has developed a cloud-based digital marketplace for farmers to list services, trade their commodities, and get the best price for their products. The application also helps with post-harvest quality analysis using advanced ML models and generates instant reports to vet the quality of their agri produces by analyzing photographs.To get to know more about the solution, read the full case study, here.