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Artificial Intelligence: the future of agriculture

Artificial Intelligence: the future of agriculture

Imagine a robotic lens zooming in on the wheat sprout. It captures images and sends them to an AI (Artificial Intelligence) platform that predicts precisely how long it will take for it to mature. The advantages of having information like when to plant, pick and pack and the quantity that will be in the market makes everyone’s job easier and the value chain more efficient. This is only one example of emerging role of AI in revolutionising agriculture. In this context, AI has the potential to help confront one of humanity’s biggest upcoming challenges of feeding extra 2 billion people by 2050 as UN projections report only 4% of additional land will be available for cultivation. AI-powered technology can enable farmers to do more with less while also improving yield quality and value chain.

Therefore, in these transformative times, exciting innovations are not limited to gleaming tech campuses but spread across acres of fields as farmers attempt to mix AI and agriculture. Modifying farming practices under this new light of AI aims to benefit societies around the world.

Agriculture is a major industry and foundation for any economy especially for developing countries like Pakistan where agriculture contributes around 20% to the overall GDP and employs almost 50% of its’ population, directly or indirectly. Pakistan being a low-income developing country with a growing population has a dire need to have an efficient agriculture sector to meet its’ food and fibre requirements.

Multiple factors like climate change, population growth and growing food insecurity have propelled the private sector into seeking novel approaches to not only maintain but improve productivity. Thus, under Fourth Industrial Revolution, AI has emerged as the game changer for the sectors’ technological evolution. The agricultural ecosystem is made up of biological, physical and chemical factors and are all connected in a matching pattern. AI can help identify these patterns to predict future patterns in order to minimize the risk and maximize the benefit for the farmers.

Though, there is a need to understand potential uses of AI and its’ scope in agriculture. Present research can be translated into three major applications.

Firstly, the Agricultural Robots. There is a market to develop and program autonomous robots that have the capacity to independently handle basic agricultural tasks such as harvesting crops. They have the capacity to undertake these mundane tasks at a faster pace and greater volume as compared to humans. This also includes efficient ways such as computer vision for farmers to protect crops from weed using methods to precisely spray weeds and not affect the crop. This precision spraying can eliminate the volume of chemicals normally sprayed onto the crop.

Secondly, a major use of technology can be in monitoring and data analysis. This will tackle many issues like degradation of soil quality that is a major contributor to food insecurity and have overall negative impact on yield. Computers can use deep learning algorithms to store and process data captured by drones, GPS, field sensors, sensors installed in tractors etc. regarding potential defects and nutrient deficiencies in the soil and crop. This data can also be used to monitor the health and readiness of crop and soil by corelating the foliage patterns with certain defects and diseases. These insights about the condition of soil and crop will allow farmers to take appropriate measures.



Lastly, predictive analytics also have a role to play in the form of precision farming. Machine Learning models can be used to track and predict external factors that impact the yield such as temperature changes, rainfall, wind speed and market shifts etc. For example, an existing app in Pakistan already provides weather notifications, whereas machine learning can take this a step further by customizing the predictions based on the needs of each client. This requires quality data that is regularly updated at a rapid rate. Hence, AI can generate accurate and controlled techniques that help farmers understand and optimize harvesting times and also aid with resource management.

However, there is lack of adequate available data that is standardized and accurate for application of such technology. In order to match up all different variables and their possibilities, system requires a lot of different combinations of soil types, weather, seed variety, fertility, water availability, time period etc. As an early player in this industry, Pakistan is struggling with data availability and documenting data accurately that is essential for training the algorithm and mining the data.

Another major hurdle is that Pakistan is in very early stages of this revolution, thus, needs extensive testing and endorsement for these AI based applications. Also providing all this data will not be helpful if it cannot be analysed and understood. In order to derive this information, there is a need for experts to collect, analyse and present the data combined with clear and precise recommendations. To reap these benefits, it would also require farmers to be equipped with training to ensure they use the technologies effectively by understanding the data and insights provided by experts. Human effort combined with these tools will add value over the long haul. The farmers can move from gut-decisions to informed decisions. This new stream will foster a culture of entrepreneurship and innovation turning this traditional sector into a progressive one with higher competitive edge.

Therefore, a long-term plan combined with adequate funding is needed to test these technologies in the field. Government has the capacity to ensure agriculture credit to small farmers so they can invest in these inputs and techniques. The government also needs to introduce policies and sectoral plans that outline the long-term vision for the sector and can make the agriculture industry efficient. AI driven methods are promising to address challenges and help improve efficiency for yield by timely predicting changes and identifying new opportunities. These tools will assess operations and track progress that will have many benefits such as decreasing crop waste, improving food security, minimizing need for chemicals on the crops and using resources sustainably.

[box type=”note” align=”” class=”” width=””]The writer Amna S. Sandhu is a Research Associate, Sustainable Development Policy Institute (SDPI)[/box]

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