The agricultural sector accounts for approximately 12% of global emissions, but in lower income countries it can account for over 50% of national emissions. Thankfully, the agricultural sector’s emissions have decreased by 20% between 1990 and 2015. The majority of these reductions have come from technological adaptations to increase the efficiency of agricultural production. Gone are the days of a farmer working their land with hand tools, artificial intelligence now allows farmers to improve their efficiency and reduce their environmental impact. With artificial intelligence’s computational power increasing and the adoption cost falling (Patrício & Rieder, 2018), it is becoming more viable, especially for smallholder farmers.
Artificial intelligence can improve agricultural efficiency in a number of ways. First it can determine the quality of grain crops (Patrício & Rieder, 2018). Traditionally, farmers would have to manually assess grain, checking for diseases, pests and the overall quality of the crop. However, this process is costly, time consuming and susceptible to human error (Zareiforoush et al., 2015). Additionally, human inspection can lead to lower yields as crops are damaged during inspection.
Artificial intelligence not only offers the possibility to reduce the cost and time taken to carry out the inspection, but also allows much more to be done with the data collected (Bolandnazar et al., 2019). The technology can rapidly determine the disease or pest, can recommend a course of action and the scale that is required to tackle the problem. With this information, solutions can be found quickly and the problem rectified with minimal environmental cost. This monitoring is also much less intrusive in comparison and therefore reduces crop waste.
Another benefit that artificial intelligence has is its ability to predict crop yields. It can do this by monitoring seed germination and health, while also taking into account the farms resources and inputs using artificial neural networks (ANNs) (Horak, 2019). The reverse is also true, ANNs can indicate what inputs are necessary to achieve a desired yield.
By having a clearer understanding of the inputs necessary, it makes farming more efficient and minimises waste. Artificial intelligence has so much to offer the agricultural sector and can monitor variables at a level of detail humans cannot compete with. It can provide real time information on plant health, soil quality and weather conditions allowing for automated adjustments to occur. This will boost the yield while minimising energy expenditure, a win-win for farmers and the planet. Particularly important for irrigation which accounts for 80% of agricultural input energy (Bolandnazar et al., 2019).
Another benefit of artificial intelligence is its ability to create public databases, that can inform farm management (Bolandnazar et al., 2019) and encourage the adoption of sustainable practices. Each farm will have a different management strategy, so by sharing this information, it can expose farmers to methods they can adopt to boost their efficiency. In turn improving the sector’s efficiency as a whole. This ensures that the agricultural sector is championing best practice and will have constantly advancing standards as farms continue to innovate and share.
The agricultural sector has a lot of responsibility when it comes to climate change and needs to be reformed, especially in countries aiming for carbon neutrality. Artificial intelligence presents the perfect opportunity for this to take place. We need to encourage more research and development, rather than shying away from technology. Its benefits will far outweigh the costs. It is also important that sufficient education comes with the technology, so it is used to its full potential. The benefits are not just constrained to higher income countries either, even the basic ability to get real time weather updates can have a huge impact on agricultural practices for smallholder farmers, in lower income countries.
I for one welcome the age of technological revolution that we live in. These advancements will allow us to protect the environment, while maintaining a healthy economy. A crucial combination for our future.
Bolandnazar, E., Rohani, A. & Taki, M., 2019. Energy consumption forecasting in agriculture by artificial intelligence and mathematical models. Energy Sources, Part A: Recovery, Utilization and Environmental Effects.
Fellmann, T. et al., 2018. Major challenges of integrating agriculture into climate change mitigation policy frameworks. Mitigation and Adaptation Strategies for Global Change, 23(3), pp.451–468.
Horák, J., 2019. Using artificial intelligence to analyse businesses in agriculture industry. SHS Web of Conferences, 61, pp.SHS Web of Conferences, Vol.61.
Patrício & Rieder, 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, pp.69–81.
Zareiforoush, H. et al., 2015. Potential Applications of Computer Vision in Quality Inspection of Rice: A Review. Food Engineering Reviews, 7(3), pp.321–345.