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Revolutionising sustainable agriculture

Through AI

Artificial Intelligence (AI) is taking the world by storm. Recent developments now allow scientists to integrate AI into sustainable farming. Through transforming the way, we grow crops, manage resources and pests, and most importantly- protect the environment.


There are many applications for AI in agriculture. Outlined below are some of the areas in which the incorporation of AI systems improves sustainability:

Precision farming

Artificial intelligence systems help improve the overall quality and accuracy of harvesting – known as precision farming.

Artificial intelligence technology helps detect plant diseases, pests, and malnutrition on farms. AI sensors can detect and target weeds, then decide what herbicide to use in an area. This helps reduce the use of herbicides and lower costs. Many tech companies have developed robots that use computer vision and AI to monitor and precisely spray weeds. These robots can eliminate 80% of the chemicals normally sprayed on crops and reduce herbicide costs by 90%. These intelligent AI sprayers can drastically reduce the amount of chemicals used in the field, improving product quality, and lowering costs.

Vertical farming


Vertical farming is a technique in which plants are grown vertically by being stacked on top of each other (usually indoors) as opposed to the ‘traditional way’ of growing plants and crops on big strips of land. This approach offers several benefits for sustainable agriculture and waste reduction. The use of AI brings even more significant advancements making it more sustainable and efficient-


Intelligent Climate Control: AI can use algorithms to measure and monitor temperature, humidity, and lighting conditions to optimise climate control in vertical farms. Thus, reducing energy consumption and improving resource efficiency. Creating an enhanced climate-controlled environment also allows for repeatable and programmable crop production.


Predictive Plant Modelling: The difference between a profitable year and a failed harvest can just be the specific time the seeds were sowed. By using AI farmers can use predictive analysis tools to determine the exact date suitable for sowing seeds for maximum yield and reduce waste from overproduction.


Automated Nutrient Monitoring: To optimize plant nutrition, AI systems monitor and adjust nutrient levels in hydroponic (plants immersed in nutrient containing water) and aeroponic setups (plants growing outside the soil, with nutrients being provided by spraying the roots).

Genetic engineering

AI plays a pivotal role in genetic engineering, enhancing the sustainability and precision of crop modification through-

Targeted Gene Editing: AI algorithms help in gene editing to produce desirable traits in crops, such as resistance to disease or improved nutritional content. This allows genetic modification without the need to conduct extensive field trials. Thus, saving time and resources.


Computational Modelling: By combining AI modelling with gene prediction, farmers will be able to predict which combinations of genes have the potential to increase crop yield.


Pest management and disease detection

Artificial intelligence solutions such as smart pest detection systems are being used to monitor crops for signs of pests and diseases. These systems detect changes in the environment such as temperature, humidity, and soil nutrients, then alert farmers when something is wrong. This allows farmers to act quickly and effectively, taking preventive measures before pests cause significant damage.


Another way to achieve this is by using computer vision and image processing techniques, AI can detect signs of pest infestation, nutrient deficiencies and other issues that can affect yields. This data can help farmers make informed decisions about how to protect their crops.


By incorporating AI into these aspects of sustainable agriculture farmers can achieve high yields, reduce waste and enable more sustainable farming practices, reducing environmental impacts while ensuring efficient food production.

By Aleksandra Zurowska

Related article: Digital innovation in rural farming

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