In an increasingly interconnected world with urgent global challenges, the Sustainable Development Goals (SDGs) established by the United Nations for the year 2030 stand as a crucial roadmap to address problems such as social inequality, sustainable economic growth and environmental protection at a global level. Colombia, like other nations, faces the challenge of aligning its policies and actions with these objectives to move towards more inclusive and sustainable development. 

On the other hand, the fourth industrial revolution, characterized by the convergence of technologies that erase the borders between the physical, the digital and the biological, is rapidly transforming economic sectors worldwide. In particular, the integration of 4.0 technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), in the agricultural sector promises to improve productivity and efficiency, thus contributing to the fulfillment of the SDGs. 

In the national context, the agricultural sector plays a crucial role in the economy, especially in a country rich in natural resources like Colombia. Despite the economic and social challenges, the agricultural sector has demonstrated its resilience during the crisis triggered by the COVID-19 pandemic, maintaining positive figures and supplying national food demand. However, significant challenges remain in terms of social inequality, economic growth and environmental sustainability that must be addressed. 

On February 22, 2024, Yeapp, together with our partners Yeapdata and Tecnicaña, developed the event “Technical talk: Exploring advances in AI and cybersecurity for the agro sector”. In this blog we share memories of the event and mention some initiatives proposed to integrate 4.0 technologies and promote development and innovation in the Colombian agricultural sector. 

Agro 4.0 Project C4IR.CO

The Agro 4.0 project, promoted by entities such as MinTic and the Center for the Fourth Industrial Revolution of Colombia (C4IR.CO), emerged in response to the need to modernize and improve the productivity of the Colombian agricultural sector through the integration of emerging technologies. Through the implementation of 10 pilots in strategic crops such as cocoa, coffee and avocado, we sought to evaluate the impact of artificial intelligence and the internet of things in improving the efficiency and profitability of small and medium-sized businesses. producers. 

During the execution of the project, several challenges were identified that represented important obstacles to its implementation. Among the main challenges were the lack of access to advanced technologies in some rural areas, resistance to change on the part of some farmers and the need to train and sensitize the actors involved about the potential of technology in the agricultural sector. In addition, difficulties were faced related to the interoperability of the different technological solutions and the need to adapt them to the specific conditions of each crop and region. 

Despite these challenges, the Agro 4.0 project managed to obtain important conclusions that highlight the potential of technology to improve the productivity and sustainability of the Colombian agricultural sector. It was shown that the integration of technologies such as artificial intelligence and the Internet of Things can generate positive impacts on the efficiency of resource use, decision making and crop quality. However, it is recognized that there are still outstanding challenges in terms of infrastructure, training and public policies that must be addressed to guarantee a broader and more effective adoption of these technologies in the Colombian agricultural sector. 

Benefits of using AI in agriculture

In the agricultural sector, the implementation of artificial intelligence offers a series of benefits that cover each phase of the value chain. In the cultivation or production phase, AI provides solutions to increase the precision and effectiveness of agricultural practices, which translates into increased productivity and process optimization. For example, through predictive models on seasonality and crop rotation, AI helps farmers make informed decisions that maximize yields. 

In the laboratory, AI powers data analysis and studies to ensure quality and increase productivity. This is evidenced in the analysis and learning of inputs to achieve high-quality organic crops, as well as in the early detection of pests and diseases that can affect crops. This advanced analytics capability allows farmers to take preventive and corrective measures more efficiently, protecting their crops and improving profitability. 

In the distribution and logistics phase, AI is used for automation of the distribution chain and optimal tracking of products. This guarantees security, transparency and reliability throughout the agricultural supply chain, from production to the final consumer. Furthermore, the development of digital platforms and AI-based markets facilitates the connection between producers and users, eliminating intermediaries and optimizing sales processes, which translates into greater income for farmers. 

AI use cases in agriculture

Now, we will explore five use cases that illustrate how artificial intelligence has been successfully implemented in the agricultural sector, mentioning existing solutions that address various problems and optimize processes in the value chain. 

• • Crop yield prediction: Using machine learning algorithms, AI can analyze historical crop data, weather conditions, soils and other factors to predict future crop yields. A prominent example is Microsoft's "FarmBeats" platform, which uses sensors and drones to collect real-time data and provide accurate recommendations to farmers. 
• • Early detection of diseases and pests: By analyzing images and data collected by drones or cameras installed in the field, AI can identify signs of diseases or pests in crops before they are visible to the human eye. The American company “Carbon Robotics” developed autonomous agricultural robots equipped with artificial intelligence and computer vision systems to optimize various tasks in the field, such as detecting and controlling pests, applying fertilizers and herbicides, and harvesting crops. 
• • Optimizing water management: AI can analyze data from soil moisture sensors, weather forecasts and other factors to optimize irrigation scheduling and minimize water waste. The company "AvidWater" offers a platform that uses AI to monitor and manage water use in crops, helping farmers make data-driven decisions for more efficient irrigation. 
• • Improving soil quality: Using predictive models and data analytics, AI can provide personalized recommendations for soil management, including the application of fertilizers and amendments. "Trace Genomics" uses DNA sequencing and AI to analyze soil samples and provide farmers with detailed information on soil health and specific recommendations to improve soil quality. 
• • Agricultural supply chain optimization: By analyzing inventory data, market demand, weather conditions and other factors, AI can help optimize the logistics and distribution of agricultural products. The “AgShift” platform uses AI to analyze the quality and freshness of agricultural products during transportation and storage, helping to minimize waste and improve supply chain efficiency. 

YeappAgro 

Yeapp also developed its own solution in which AI is integrated into the agricultural sector. This solution is YeappAgro, a predictive tool designed for farmers that suggests the optimal time to carry out activities such as fertilizing, watering, protecting against pests and harvesting, with the aim of improving production, efficiently managing resources and maximizing profits. Using learning sources, sensors and weather stations, as well as expert learning, YeappAgro offers different types of recommendations for each stage of the growing cycle. With an effectiveness of 86% in the designed model, this tool has demonstrated its ability to improve decision making in agriculture. 

In addition to its application in crops such as coffee, avocado or sugar cane, YeappAgro also has the potential to be used in other industries and crops, offering predictive and personalized solutions to maximize efficiency and results in various agricultural activities. Thanks to its data-driven approach and machine learning, this tool is positioned as a powerful ally for Colombian farmers and producers, allowing them to optimize their operations and obtain better yields from their crops. 

Future of AI in agriculture

The use and application of artificial intelligence in the agricultural sector presents endless opportunities, but also faces significant challenges. Among the most notable challenges is the need to guarantee the accessibility and democratization of these technologies for all farmers, especially those in less developed regions or with limited resources. Furthermore, it is essential to address concerns related to data privacy and cybersecurity in the agricultural context, as well as promote adequate education and training so that farmers can make the most of these tools. Despite these challenges, the future of using artificial intelligence in agriculture is promising, offering the potential to increase productivity, reduce resource waste, improve sustainability, and contribute to global food security. It is crucial to continue investing in research and development, as well as the implementation of policies and regulations that encourage the responsible and ethical use of artificial intelligence in the agricultural sector, ensuring that it benefits both farmers and the environment.