According to a united nation report, the human population of the world is projected to reach 9.8 billion by 2050, and there is approximately 8.0 billion people in 2022. The statistics show that there will be approximately a 20% hike in the human population. One main domain on which human life depends is agriculture, and to complete this gap between the population of today and of 2050, this domain will be required to increase its productivity by 60%. In India alone, growing, processing and distributing food is a 71,220 Billion business.
By looking at today’s scenario, machine learning, data science, and artificial intelligence are potential ways to fulfil the anticipated food needs of people joining us by 2050. As we know that the whole agriculture process is a combination of many other processes, these sub-processes require tracking and monitoring for so long where the size of the farming area is often hundreds of acres, and at the same time, insights like weather, seasonal sunlight and attacking patterns of animal birds, insects are always required to be better modelled. There is plenty of such information that needs to be managed simultaneously, and here machine learning finds itself as a perfect solution to the problem of managing a large size of information at the same time.
The above-given information is why farmers, co-ops and agricultural companies are largely interested in investing in AI. According to a BI Intelligence research report, global spending in agriculture industries on AI and machine learning is projected to reach about $4 billion, which was $1 in 2020. These numbers represent how data-centric approaches are expanding their ways of being applied in the field. The following are use cases AI finds in agriculture.
AI and Machine Learning-Based Surveillance Systems
Applying AI and machine learning models in crop surveillance systems enable farmers to monitor crop fields in real-time and easily identify the animal and human breaches. This way, AI and Machine learning help reduce the chances of the crop being destroyed. These systems record video and give rapid responses based on video analytics. These AI-enabled solutions can be scaled from small farmers to large-scale agriculture operations.
Crop Yield Prediction
Applying surveillance systems on fields can also be used for collecting data, and this helps ML algorithms to analyse real-time video streaming. Also, thanks a lot to IoT(Internet of Things) that helps collect the in-ground data of moisture, fertiliser and natural nutrient levels.
Combining both data AI and ML helps analyse each crop’s patterns over time, as machine learning is a perfect way to combine massive data sets and provide constraint-based advice about crop yield. Also, more advanced level Ai enabled systems can help farmers to improve crop health.
Crop Planning
Yield mapping is a term that involves agriculture and machine learning algorithms together. In-depth, we can say that supervised machine learning models help to find patterns in large agricultural datasets. This helps in understanding the orthogonality of crops in real-time. All these things play a crucial role in crop planning.
With the help of machine learning, it becomes easy to quantify the potential yield rates of a field even before the vegetation is started. To make such predictions, algorithms use social condition data from the in-groud sensor and surveillance-based data of soil colour and atmosphere and help the agricultural specialist to predict soil yields for a given crop.
Pest Management
A former or agricultural organisation can improve pest management by combining surveillance data and in-ground sensor data. International agencies use infrared camera data from drones and in-ground sensor data to monitor crop health and leverage AI to predict pest infestations.
Robots
Various AI and ML-based smart tractors, agribots and other tools help formers to recover the shortage of agricultural workers. These tools and devices are a viable option for many remote agricultural operations. These devices came out as a blessing for large-scale agriculture businesses that not only reduces the cost of employees but also gives higher accuracy in repetitive tasks.
Supply Chain Management
Just like in other supply chain management systems, AI has accelerated the track and traceability across all agricultural supply chains. More impact of this can be seen in the pandemic times. AI and ML models are not only accelerating apply chains but also helping in inventory management where they can easily differentiate between the inbound and outbound shipments’ batch, lot and container level assignments of crops and material.
In a more advanced system, sensors are used to differentiate between the shipment’s and material’s condition.
Pesticides Optimisation
Machine learning models are being utilised to find out the right mix of pesticides and their application in a specified field area. Utilising such models, formers are not only finding the right fit of pesticides but also reducing costs. Using the sensor data and visual data from drone models can detect and segregate defective areas into different classes. Also, these models can tell the right mixes of pesticides for different defective areas.
Final words
Here in the above, we have seen some of the most common use cases of AI and Ml in the agriculture domain. Instead of these, machine learning and AI can also be used to predict the market price of different crops based on the crop yield rate and help us optimise irrigation systems.
When talking about the agriculture domain, we at DSW | Data Science Wizards find endless opportunities for AI and machine learning in the domain that can make it more advanced. Our flagship AI platform UnifyAI has the capabilities of solving AI and ML use cases of any domain in an optimised and accurate way and in reduced timing. We understand that a larger part of the Earth is dependent on agricultural businesses, so it becomes more important to resolve AI use cases in this sector with higher accuracy. Using UnifyAI, we make sure that AI and ML play an important role in advancing our client’s agricultural businesses and helping them to perform easy and effective farming with lesser effort.
About DSW
Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.
DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.
Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai