Agriculture is the mainstay in many countries, and with the rising population, the food demand is projected to witness a 35-56 per cent surge by 2050. And that’s not to mention that land and water have become critical resources, and climatic changes create a major need for improvements. As the world moves towards rapid digitalisation, technological tools can be required to reduce food wastage and supply chains to meet the burgeoning demand for food production globally.  

Technological advances are bringing forth dramatic changes in the Indian agriculture industry. As the rural population moves to urban cities for better employment opportunities, technology has become a robust solution to remove the bottlenecks in agriculture profitability and productivity. AI-driven tools and systems in agriculture continue to bring positive changes in the farming sector, rendering smart solutions for the industry.  

The United Nations Food and Agriculture Estimate suggests – the world’s agricultural yield has to be increased by 70 per cent by 2050 with the current landholdings. Considering the expanding demand, AI-driven solutions can enhance agricultural outputs with ease, provided farmers can overcome social and economic challenges. From computer vision technology to predictive analysis for healthy crop production, the industry is entering a new era of evolution.  

Crop and Soil Monitoring 

For yielding better crop produce, AI-driven automatic systems monitor soil and crop based on different variables such as temperature, moisture levels, soil nutrient levels, sunlight, etc. The data gathered is further analyzed by Machine Learning algorithms that can provide optimal insights about the condition of crops and soil. For instance, AI and ML algorithms can be used to determine the need for water and nutrients that can alert farmers to meet the requirements of a healthy crop. It can also detect the possibility of pests and diseases that affect the output.  

Integration of AI in agriculture can help farmers monitor the condition which is not optimal for plant growth. This allows farmers to take preventive measures and get the best timely results. As part of AI-embedded systems, computer vision technology is crucial in detecting and analyzing crop quality with precision. This reduces the scope of human errors and dependence on manual monitoring and observation. As a result, farmers can produce high-quality yields with greater efficiency at lower costs.  

Automatic weeding 

Traditionally, farmers used to practice manual processes to remove weeds, like using herbicides, making it challenging for the crop management system. AI-integrated computer vision technology intelligently identifies weeds in crop monitoring images. Apart from this, intelligent AI algorithms embedded with Machine Learning tools can develop a robot and carry out de-weeding automatically. This saves the farmers’ time in de-weeding processes through manual interventions. In addition, it saves time and cost with much accuracy, making it a healthy organic farming practice.   

Machine Learning with 5G and IoT 

The next gen of the agriculture industry lies in adopting smart farming practices. The advancements in agriculture are aided by IoT with cloud computing over a futuristic 5G network. This brings a paradigm shift in the ‘human oriented’ to a ‘humanly independent’ and much more securer farming system.  

Integrating such technology tools with AI revolutionises the entire crop cycle, from planting, to harvesting, with a completely automated monitoring system. This cycle closes the supply-demand gap while ensuring higher yields and profitability for farmers. The specialised equipment and wireless connectivity in IoT devices further reduce high operational costs, called precision agriculture.  


The advent of disruptive technologies such as AI is changing the meaning of agriculture in every way. Traditional agricultural practices are replacing smart farming techniques aided by AI tools. Gaining accurate insights can prove to be the robust driving force for increasing agricultural production. 

The author is Co- Founder & VP of Marketing & Solutions, [X}cube LABS