From cultivation to improving harvesting quality, artificial intelligence is known as one of the main elements for a surplus yield but that too for the ones who are capable enough to make use of it. Agriculture is seeing rapid adoption of artificial intelligence and machine learning, both in terms of agricultural products and in field farming techniques. Apart from that, most of the countries are looking forward to involving such techniques. In 2016, the estimated value added by the agricultural industry was estimated at just under 01 percent of the US GDP. The US Environmental Protection Agency estimates that agriculture contributes roughly $330 billion in annual revenue to the economy, thus such techniques would definitely speed things up.

Moving onto a few factual details, the artificial intelligence in agriculture appears to fall into three major categories; with the first being agricultural robots. Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at higher volume and faster pace than humans. Next up is crop and soil monitoring. Companies are leveraging computer vision and deep learning algorithms to process data captured by drones and software based technology to monitor crop and soil health. Under the predictive analytics category, machine models are being developed to track and predict various environmental impacts on crop yield such as weather changes. The ability to control weeds is a top priority for farmers and an ongoing challenge as herbicide resistance becomes more commonplace.

Today, an estimated 250 species of weed have become resistant to herbicides. In a research conducted by the Weed Science Society of America on the impact of uncontrolled weeds on corn and soybean crops, annual losses to farmers are estimated at 43 billion dollars. Companies are using automation and robotics to help farmers find more efficient ways to protect their crops from weeds. A technology known as “Blue River Technology” has developed a robot called See and Spray which reportedly leverages computer vision to monitor and precisely spray weeds on cotton plants. Precision spraying can help prevent herbicide resistance.

Automation is also emerging in an effort to help address challenges in the labour force. Companies such as Harvest CROO Robotics have developed a robot to help strawberry farmers pick and pack their crops. Such robots can harvest 8 acres in one day and replace 30 human labourers. With that being said, the development of soil analysis machines might become essential in the upcoming years due to deforestation which makes soil infertile for deforestation and causes water-logging and salinity. A system needs to be developed which uses machine learning to provide clients with a sense of their soil’s strengths and weaknesses. The emphasis of such services should be on preventing defective crops and optimizing the potential for healthy crop production.

Another method which has been implemented and is highly demanded for monitoring is the use of drones. The market for drones in agriculture is projected to reach $480 million by 2027. Once again there needs to be establishment of corporations and companies that aim to help users improve their crop yield and to reduce costs. Users should be able to programme the drone’s route and once deployed the device will leverage computer vision to record images which will be used for analysis. A device such as a USB can then be used to transfer the footage from the drone to a computer and upload the captured data to a cloud drive. Afterwards algorithms can be used to integrate and analyse the captured images and data to provide a detailed report. The government of Pakistan has also recently decided to establish private drone authority so as to regulate use of drones in different fields.

Irrigation depends a lot upon the weather and this is something that also affects sustainability. Machine learning algorithms in connection with satellites can be used to predict weather, analyse crop sustainability and evaluate farms for the presence of diseases and pests. A lot of farmers do complain about the fact that fertilizers do not need to be used all around fields but this has become a necessity for them. Softwares can be developed that can inform users where exactly are fertilizers needed; this can reduce the amount of fertilizer used by nearly 40 percent. Such softwares should be marketed for use across mobile phones.

Artificial intelligence-driven technologies are emerging to help improve efficiency and address challenges facing the industry including crop yield, soil health and herbicide resistance. Agricultural robots are poised to become a highly valued application of artificial intelligence in this sector. The above mentioned problems can be catered by feasible mechanisms that can reduce complaints of farmers and provide them with a better environment. It will be important that farmers are equipped with training that is up-to-date to ensure the technologies are used and continue to improve. This will help prove the value of these tools over the long. It is anticipated that the agricultural industry will continue to see steady adoption of artificial intelligence and will continue to monitor this trend.