Agriculture, from time immemorial, has served as the principal occupation of man. It has played significant roles in the provision of food and raw materials for industries around the world. Over the years, the process of farming has evolved from the use of crude tools such as hoes and cutlasses to the adoption of heavy machines like tractors for planting and harvesting crops. Today, the tides are changing faster than we can ever imagine, all thanks to the increased need for agricultural products.
Population growth, government policies on land, and other problems linked with farm inputs have greatly affected the agricultural sector. With the world population expected to reach 10 billion people by 2050, food supply may be inadequate, and arable land will be a scarce commodity, thus initiating the need for land conversation and “precision agriculture” where less land will be utilized to produce more food for the growing world population.
With the average worth of the agricultural sector capped at $5 trillion, the industry is now turning to the use of technology and AI-powered processes to yield healthier crops, organize farm data for farm input and output, control pest, survey farm implements, monitor the soil and growing conditions, assist with the farm workload, and even improve the efficiency of some agro-based processes in the food supply chain.
Traditional Vs. AI-driven Agriculture
Before the introduction of AI-based technology in precision agriculture, most agricultural products and processes relied heavily on the use of vast areas of land for the production of food and other farm outputs. This traditional method of farming needed a lot of farm inputs like fertilizers and pesticides, a large workforce for the inspection of the crops and other activities on the farmland, heavy machinery, and a host of other factors. Although these processes were effective, they increased the rate of pollution in the environment, were expensive, and above all, cannot cater for the increased need of a growing population since the available areas of land will soon be occupied, leaving little to nothing for agricultural processes. The traditional methods of farming were strictly based on trial by error where farmers only planted their crops and wait for the worst to happen. AI-driven precision agriculture brings effectiveness, safety and accuracy to the table. It utilizes farm data to calculate the exact amounts of farm inputs needed for the best production, predicts the possible weather conditions to influence the choice of crops and farming decision and above all, reduces the need for a large workforce while promoting the growth of organic and pesticide-free foods around the world.
THE ROLE OF AI-DRIVEN TECHNOLOGY IN PRECISION FARMING
Some of the benefits associated with AI-driven technologies and precision agriculture include but are not limited to the following;
a) Analysis of farm data
With the help of AI, farm data like temperature, weather conditions, soil conditions, and soil usage can be effectively collected and analyzed to influence decision making on the farm. This can make planning more productive and thus, boosting the yield of crops on the farm. With this technology, farmers can confidently select the best-performing crops based on the farm data and reduce the cost of wasting both time and resources. The amount of use of fertilizers affects the increase or decrease in production. This past data collected can be used for knowing or predicting what works and what does not. Best possible crop suggestion before the season keeping the past data and other sensor, environment data in the view.
b) Improvement of harvest quality and accuracy
Precision agriculture utilizes AI-based processes for the detection of nutrients on farmland, predicting the possible outbreak of diseases in both plants and animals. Precision agriculture can also play significant roles in weed detection and deciding on the best herbicide to apply. This can limit the over-application of herbicides and other pesticides on the farm; thus, controlling pollution while increasing food safety.
c) Increased agricultural accuracy and productivity
Gone are the days when farm work is carried out via a trial by error process. Farmers can now create a seasonal forecasting model to boost their accuracy and productivity. With these models, farmers can accurately predict upcoming weather conditions several months ahead of time and can decide on what to do. AI-based processes like deep learning algorithms and computer vision can capture farm data from UAV drones for farm inspections.
d) Automated farm processes
AI promotes the production, procession, and packaging of highly organic food in a sanitary way. Automated machines can perform farm tasks with little to no human intervention hence reducing the possibilities of cross-contamination and foodborne diseases. These systems can run scheduled checks and farms and analyze farm data for better yield. Agriculture robots can be used for doing multiple tasks intelligently by learning the output of the tasks they do and improve themselves as humans do. Present-day machines (mechanical ones) cannot replace humans in some critical tasks on the farm.
The agricultural sector is set to receive a facelift in organic farming and other aspects of safe farming with AI-driven precision farming techniques. Artificial intelligence is taking over the world one sector at a time, and there is a great promise on how it can bring effectiveness and ease to the highly labour-intensive agricultural processes.
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