Agriculture or farming, one of the oldest and most important professions in the world has come a long way. It has never lost its importance and is a major industry and often considered as the foundation of the economy of any nation. Agriculture plays a vital role in the economy of developing countries and provides the main source of food, employment, and income to their rural populations.
As per FAO (2000), it has been established that the share of the agricultural population in the total populace is 67% that agriculture accounts for 39.4% of the GDP and that 43% of all exports consist of agricultural goods. Worldwide, agriculture is a $5 trillion industry.
The industry is now turning to AI technologies to help yield healthier crops, control pests, monitor soil, and growing conditions, organize data for farmers, help with the workload, and improve a wide range of agriculture-related tasks in the entire food supply chain. Artificial intelligence (AI) is now steadily becoming a part of the industry’s technological evolution.
In this blog, we probe into the applications of AI to provide business leaders with an understanding of current and emerging trends, and present representative examples of popular applications.
AI in the Agri sector
The most popular applications of AI in agriculture can be classified into three categories:
- Agricultural Robots – Firms are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.
- Crop & Soil Monitoring – Companies are leveraging computer vision and deep-learning algorithms to process data captured by drones and/or software-based technology to monitor crop and soil health.
- Predictive Analytics – Machine learning (ML) models are being developed to track and predict various environmental impacts on crop yield such as weather changes.
Automation in weed control
Various companies and firms are specializing in agricultural robots. Here we discuss a firm which specializes in weed control. The ability to control weeds is a top priority for farmers and an ongoing challenge as herbicide resistance becomes more commonplace. 250 species of weeds are resistant to herbicides and the annual losses to farmers due to uncontrolled weeds on corn and soybean crops are estimated at $43billion.
To tackle the weed menace and protect farmers’ crops, companies are using automation and robotics. Blue River Technology has developed a robot called See & Spray which reportedly leverages computer vision to monitor and precisely spray weeds on cotton plants. Precision spraying can help prevent herbicide resistance. The firm uses its precision technology to eliminate 80 % of the volume of chemicals normally sprayed on crops and can reduce herbicide expenditures by 90 %. John Deere has acquired the company ever since.
Automation in farm labor
Automation is also emerging to help address challenges in the agricultural labor force. The industry is projected to see a 6% decline in agricultural workers from 2014 to 2024. Taking cognizance of this, Harvest CROO Robotics, an Agri Ai company has developed a robot to help strawberry farmers pick and pack their crops.
The robot can harvest 8 acres in a single day and replace 30 human laborers. It is estimated that 40% of annual farm costs are fed back into wages, salaries, and contract labor expenses.
CROP & SOIL HEALTH MONITORING
Soil conservation through Deep learning
Deforestation and degradation of soil quality remain significant threats to food security and hurt the economy. In the US alone, the annual cost of soil erosion is estimated at $44 billion. PEAT, an agricultural tech startup developed a deep learning application called Plantix that identifies potential defects and nutrient deficiencies in the soil.
The scrutiny is conducted by software algorithms that correlate particular foliage patterns with certain soil defects, plant pests, and diseases. The image recognition app identifies possible defects through images captured by the user’s smartphone camera. Users are then provided with soil restoration techniques, tips, and other possible solutions. It claims that its software can rapidly achieve pattern detection with an accuracy of up to 95%.
Satellites for Weather Prediction and Crop Sustainability
where, a Colorado-based company uses Machine Learning (ML) algorithms in connection with satellites to predict the weather, analyze crop sustainability, and evaluate farms for the presence of diseases and pests. Daily weather predictions are customized based on the needs of each client and range from hyperlocal to global with the help of satellites. The company also claims that it provides its users with access to over a billion points of agronomic data daily. Data sources include temperature, precipitation, wind speed, and solar radiation, ‘along with comparisons to historic values for anywhere on the agricultural earth.’
Another company that pioneers in monitoring crop health and sustainability with the help of satellites are FarmShots. It focuses on analyzing agricultural data derived from images captured by satellites and drones for detecting diseases, pests, and poor plant nutrition on farms. The tech startup’s software informs users exactly where fertilizer is needed and can reduce the amount of fertilizer used by nearly 40 %. The software is marketed for use across mobile devices.
AI-driven technologies are emerging to help improve efficiency and to 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. It is also feasible that agricultural robots will be developed to complete an increasingly diverse array of tasks shortly.
It will be important that farmers are equipped with up-to-date training to ensure the technologies are used and continue to improve. Extensive testing and validation of emerging AI applications in the Agro sector will be critical as agriculture is impacted by environmental factors that cannot be controlled, unlike other industries where risk is easier to model and predict. We anticipate that the agricultural sector will continue to see the steady adoption of AI.