Precision Agriculture and the Role of AI

What is Precision Agriculture?

Global population is on the rise and food production must keep up with it. By 2050, there will be almost 10 billion humans alive on the planet and food supply must grow by at least 98% to ensure the species' continued survival. Optimizing agricultural yield to sustain the world’s growing population requires refining existing agricultural operations to maximize output and minimize expenditure. In this regard, precision agriculture offers a bright ray of hope.

Precision agriculture is an information and technology management system that utilizes several data point sources such as soils, moisture, nutrients, crops, etc to observe, measure, and respond to crop variability. The overall goal is to create an information system for decision making that aids resource management and maximizes returns.

The concept of precision agriculture has existed for some time now but is only now gaining a foothold due to technological advancements, notably in Artificial Intelligence, Global Positioning System, and Satellite Navigation. Precision agriculture, otherwise called satellite agriculture or site-specific crop management, has a considerable economic benefit for the farmer and is also environment-friendly. Due to its prioritization of the efficient allocation of resources, it promotes the conservation of water, fertilizers, and pesticides. It further reduces the area of farmland covered by heavy agricultural equipment thereby reducing soil erosion and compaction.

Precision agriculture refines the art and science of crop management, ensuring that crop cultivation is a well-tailored process that delivers the exact needs of the crop.

Precision Agriculture and Artificial Intelligence

In tandem with IoT, AI can refine the harvesting process by guaranteeing the simultaneous and seamless operation of combine harvesters. It can notify the operators of the machines when they are in close proximity to each other and in the instance of harvesting grains, it can calculate the percentage efficiency of grains harvested per area of land. It can be used to provide real-time estimation of crop moisture, grain quality and yield per square meter.

Computer Vision is an important area that shows the close-knit relationship between Precision Agriculture and AI. Machine learning algorithms can analyze videos and images captured by satellite imagery to provide information on intra-farm health variability. The analysis can also guide the precise placement for seed planting and the application of agrochemicals.

Agrobots equipped with computer vision technology can play an important role in disease and pest identification. The algorithms are sophisticated enough to identify diseased leaves and lurking pests on the farm. They can also be applied to automate the harvesting of fruits as they are trained to distinguish between ripe and unripe fruits.

Drones that are fitted with sensors can facilitate the precise application of herbicides and pesticides. As they carry out scheduled spraying, they can also, in real-time, send live updates to the farmer who can remotely adjust the quantity or area of farmland to be sprayed.

Data Analytics is a crucial area in Precision Agriculture as a ton of data is generated from the several data points. The insights can be processed and used to shape farm management decisions as required.

Challenges to Precision Agriculture

Precision Agriculture is a highly data driven field. Not many farmers – or even government Ministries – have an ingrained data culture. For this reason, it may be challenging for the relevant stakeholders to dedicate the commitment required to ensure that the necessary structures for regular data collection are in place.

The widespread availability of the technology at a cost that is affordable to farmers in rural areas is also another hurdle to overcome.  


UNDP (2021). Precision Agriculture for SmallFarm Holders.

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