Technology is reshaping most activities humans do today. Agriculture is no different. Concepts like Smart Farming have gained prominence as newer methods for crop and farm management are on the rise. It is making farming an efficient and profitable activity. Going by the estimates, there will be a 15% increase in the demand for agricultural products in the coming decade. Using tech solutions to cope up is an ideal way forward. This article takes you through the concept of crop yield prediction with the help of spatial analysis through IoT devices.
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Crop yield refers to the amount of agricultural production reaped in one unit of land area. The measurement is usually applicable for grains and cereals and measured in tons or pounds per acre.
Agricultural producers take into account the amount of harvest per unit area for measurement. The extrapolation for the entire farm then gets done based on the harvested weight of the crop.
Crop yield prediction is a prime use case in spatial data science and start-ups, government agencies, and academic institutions are using Landsat and satellite imagery for data-driven decision-making. Satellite imagery data helps in the generation of predictive algorithms.
These algorithms help in the understanding of soil’s moisture and nutrient capabilities. It is also beneficial in identifying limiting factors for crop production.
Climate and its unpredictability are some of the factors behind the reduced rate of crop production. Weather forecasting is thus essential for improved management of crops. Agriculture has several other related industries like the sugar industry that depends on the sugarcane farmers.
So, crop yield prediction with machine learning or AI also helps them in planning the logistics of their business. The application of AI and the use of related IoT devices in agriculture is thus wide.
Let’s take a look at the benefits of crop yield prediction with AI/ML
There are various aspects when it comes to crop yield prediction. Some of these include studying climate data, satellite imagery, soil conditions, and possibilities of pest attacks. All of these combine to give a holistic overview of the suitable time frames for crop production. There are also what-if scenarios, and alternative action plans to tackle any unforeseen problems.
Businesses from the AgTech industry today are making use of neural network algorithms to predict crop yield. The backpropagation algorithms help in identifying the appropriate weight value of the yield to calculate the error derivative. Accuracy of crop yield estimation is significant for agronomic production reasons.
So, predicting crop yield is essential for the food production ecosystem around the world. With better data in hand, it becomes possible to make informed decisions. Government agencies also find the crop yield prediction data useful as they can plan accordingly for national food security.
According to the estimates of the Food and Agriculture Organization of the United Nations, the global population will increase by 2 billion. It will require a 70% jump in food production capacities. The agricultural systems around the world face a lot of challenges. Let’s take a look at what they are and how they affect yield output.
Low yield continues to be a problem, especially in developing countries like India. Poor farming infrastructure, farm size, and no proper use of technology and pesticides are a few factors behind this. The small size of farms also means that resources like irrigation facilities and financial support remain restricted.
A fluctuation in climate conditions is enough to impact the yield. It directly affects the cash flow and doesn’t allow further investments to improve productivity and mitigate risks. There is a need for AgTech companies to focus on solutions that help predict yield based on factors like climate.
Changes in average temperatures, rainfall levels, heatwaves, CO2 levels in the atmosphere, and ozone concentrations at ground level are climatic factors affecting agricultural production. Climate change affects countries unevenly. Countries with low altitudes will likely face more problems with crop production.
There is a need for tech solutions that predict fluctuating climatic conditions. Weather forecasts based on statistics use historical data to establish a pattern between different seasons.
There is thus a possibility of predicting summer temperatures based on the winter data. If there is a fluctuation in summer temperatures, it is likely that winters will also have some change.
The depletion of nutrition in the soil is a concern that adversely affects crop yield. It also affects the soil quality and is a threat to sustainability in the long term. The problem generally happens because of the erosion of the top layer of soil, where most nutrients are present.
Soil sensors that can detect nutrient levels are essential to overcome this problem. These sensors analyze the soil condition and generate timely alerts for preventive actions. It ultimately helps in improving the crop yield.
It is a problem in a developing country like India because of its dense population and intensive cultivation. One of the reasons behind it is the inheritance laws. Fathers have to distribute their land equally among their sons. The distribution leads to fragmentation, with different tracts having different fertility levels.
The best solution to avoid this problem is to avoid fragmenting the land. However, when that is not possible, the next best option is to have a system for zone detection and geo-tagging.
Satellite imagery data can monitor soil conditions for identifying the best scenarios for crop production. It further helps in checking the natural environment of a farm for the targeted use of fertilizers.
Floods lead to the washing away of a lot of agricultural lands, leading to widespread losses. The biggest impact is waterlogging in areas where crops are planted. Crops do not survive under a heavy deluge of water. So, when production gets affected, it also leads to scarcity. It ultimately leads to inflation in the prices of agricultural produce.
Weather sensors that predict uneven patterns of rainfall are essential to overcome this problem. They will send timely alerts that will help farmers propose mitigating measures. It can include the creation of soil bunds to protect the field from unexpected floods.
Much like any other sector, AI is helping agriculture by creating a platform to analyze aspects like weather and soil conditions in real-time. Our Spatial analysis solutions take historical data and sensor data from IoT devices and satellite images to identify insights for improved production of crops.
Sensors inspect various elements of soil like its moisture content and pH level. A related app gives a complete walkthrough of various sections of the field and crop health.
Here are various ways through which AI is positively impacting farming.
LANDSAT data has helped in agricultural monitoring since 1972. Whether it is about estimating crop production or monitoring water usage, LANDSAT imagery is of great importance. Besides, they are also beneficial in field-level management to identify different conditions and increase yield through zone mapping.
Predicting the weather is possible through IoT-based sensors that use historical data. The past data will always have a pattern, based on which the system will predict future weather conditions. It becomes ideal for farmers as they can find a suitable time to sow seeds and harvest the crop.
GeoSpatial AI analysis help in monitoring and identifying crop health through drone-based imagery data. Drones capture field data and transmit it to computers for further analysis. The system has algorithms to analyze images and understand farm health. The benefit is the identification of pests so that mitigating measures help overcome the problem.
It has become a vital aspect of precision farming in recent years. IoT devices can study crop rotation, water management, pest attacks, nutrition management, and much more. The devices then generate rich insights through spatial analysis that aid improved standards of crop production.
AI-trained robots are apt at mimicking multiple tasks performed by farmers. The efficiency of these robots is better as they do not get tired or make errors that humans would usually do. These robots can check crop quality and detect weeds.
Gramener actively works with AgTech companies all over the world to build Machine Learning-based Yield, Prediction models. We focus on building niche solutions for our clients by working on openly available satellite imagery datasets. Our deep learning solution can predict crop yield with high spatial resolution several months before harvest.
Want to know more about how we can help you utilize data to predict and enhance your crop yield?
Contact us for custom built low code data and AI solutions for your business challenges and check out spatial analytics solutions built for our clients, including Fortune 500 companies. Book a free demo right now.
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