Agriculture Forecasting Tool to Predict Growth & Risk Management
The agricultural industry is witnessing drastic changes as technology is updating. Companies want to implement cutting-edge technologies so that they can innovate the world and generate maximum revenue in return with less investment. In today’s world, agribusinesses are using IoT devices & computer vision to manage business risks by avoiding unpredicted events with the assistance of agriculture forecasting tools.
Agriculture businesses collect billions of bytes of data every day through different smart farming strategies including weeding robots, machine navigation, material handling, and harvesting robotics. This data can be molded into future decisions in a better fashion. Now, Let’s talk about how data analysis in smart agriculture maximizes profits.
Agriculture businesses collect billions of bytes of data every day through different smart farming strategies including weeding robots, machine navigation, material handling, and harvesting robotics. This data can be molded into future decisions in a better fashion. Now, Let’s talk about how data analysis in smart agriculture maximizes profits.
Smart Agriculture Data Analysis: Step to Digital Innovation
In data analysis, agriculture data is transformed into useful information and insights. It has multiple approaches to solving a particular problem or answer to business growth-related queries through data.
For instance, you have access to a database of a farming company. Their executives reach out to you and want some analysis of their data. “Hey! We want to know how much revenue we lost in the most recent quarter of the year.
Why did we lose it? and What’s the solution to this problem? Thus, data analysts/data scientists use different Artificial Intelligence, Machine Learning, and Statistical techniques like forecasting to answer these queries.
For instance, you have access to a database of a farming company. Their executives reach out to you and want some analysis of their data. “Hey! We want to know how much revenue we lost in the most recent quarter of the year.
Why did we lose it? and What’s the solution to this problem? Thus, data analysts/data scientists use different Artificial Intelligence, Machine Learning, and Statistical techniques like forecasting to answer these queries.
Why Does Agriculture Need Forecasting?
Forecasting or prediction is a technique that is used on data via data science to predict future outcomes or instances. Let’s say you own an agriculture business where you are dependent on the weather. If someone tells you the approximate weather for the upcoming two weeks or months. It will not be less than a miracle to you. Merely, these are the forecasting techniques that enable companies to predict upcoming events and react accordingly.
Moreover, an agriculture forecasting tool performs data analysis on agriculture datasets that agribusinesses provide to their underlying data strategists. It enables forecasting of upcoming business risks and solutions to naive problems that may hurdle in the future. On-time risk mitigation becomes an unseen force to stakeholders when they make decisions.
Moreover, an agriculture forecasting tool performs data analysis on agriculture datasets that agribusinesses provide to their underlying data strategists. It enables forecasting of upcoming business risks and solutions to naive problems that may hurdle in the future. On-time risk mitigation becomes an unseen force to stakeholders when they make decisions.
Risks to Agriculture Industry
The risks of a business could be of any type either strategic, financial, or operational but every single of them affect business efficiency and return equally. In the agriculture industry, farmers, Agri investors always feel uncomfortable because it depends on some unseen factors. In the current era of digitization, the agriculture industry still has some risks associated with it.
Volatile Commodity Pricing and Production
Commodity prices are volatile as there are countless factors associated with them on regular bases. In agriculture or farming, it’s hard to predict production because of seasonal plant diseases, soil fertility, and farming of seasonal/non-seasonal food. If we talk about price volatility in different continents like Asia, Europe, or Australia.
The prices are declared by the respective state or regional corporations. Thus, it’s difficult to overcome this risk to agriculture. But Odyx yHat is taking these factors into consideration to predict the volatile prices of commodities.
The prices are declared by the respective state or regional corporations. Thus, it’s difficult to overcome this risk to agriculture. But Odyx yHat is taking these factors into consideration to predict the volatile prices of commodities.
Extreme Weather Impacting Crops
Instability in agricultural production due to extreme weather increases the risks of crop failure on a large scale. There are various types of seeds that grow in optimal weather conditions and soil quality index.
Consequently, unconditional circumstances result in the loss of millions of dollars to small and massive farming companies. This risk could be avoided through on-time weather prediction through time series analytics and data forecasting.
Consequently, unconditional circumstances result in the loss of millions of dollars to small and massive farming companies. This risk could be avoided through on-time weather prediction through time series analytics and data forecasting.
Agriculture Financial Risks
In continents like Asia & Europe, approximately 80% of counties earn 65% of their revenue through agriculture. In agriculture, small farmers and local Agri firms take debits for harvesting through various government schemes and interest loans. Financial risks arise when agriculture businesses lose due to unseen disasters like less production, substandard fields, and more.
Supply Chain Risks
The supply chain risks arise when Agri operations encounter disruption due to economic crises or political influence. Agricultural products are difficult to transport between two terminals because they must be preserved before they can be imported or exported. To overcome the supply chain risks as above Odyx yHat is the perfect companion for your business to propel it to new horizons.
Not Using Data Collected Through Smart Farming
As smart farming is radical to digitalization in Agri but companies are unsure how to use data collected from smart farming for business well-being. Farming companies are unsure how to draw fruitful insights for non-technical executives.
Here agriculture forecasting tools like Odyx yHat come into the scenery and promise to solve every novel problem with the help of Agri data from IoT sensors and on-field installations.
Here agriculture forecasting tools like Odyx yHat come into the scenery and promise to solve every novel problem with the help of Agri data from IoT sensors and on-field installations.
Growth Prediction & Risk Mitigation Using Agriculture Forecasting Tool
Agriculture forecasting tools like Odyx yHat enable businesses to forecast volatile crop prices, product demands, and weather conditions via time series forecasting. The forecasting tools help decision-makers to create an optimal solution based on the availability of data.
Data Engineers and Data Scientists create solutions to novel problems via deep data insights. Hence, it results in growth estimation through business intelligence dashboards, predictive modeling, and data automation to enable the system to suggest challenges, solutions, and possible outcomes.
Data Engineers and Data Scientists create solutions to novel problems via deep data insights. Hence, it results in growth estimation through business intelligence dashboards, predictive modeling, and data automation to enable the system to suggest challenges, solutions, and possible outcomes.
Odyx yHat: An Exclusive Agriculture Forecasting Tool
Odyx yHat is a self-service time series forecasting tool that generates fruitful insights without any technical staff involvement. This machine learning tool encompasses a bundle of pre-built machine learning models trained to forecast upcoming events with almost 80% accuracy.
If we talk about the technical process of ML model building. It can also help to create a customized model building using machine learning techniques like classification, regression, dimension reduction, tree-based methods, logistic regression & more.
Further, Odyx yHat has the ability to handle the following use cases of data science and data analytics to think beyond.
- Commodities Supply & Demand Forecasting
- Real-time Wheat Price & Sales Forecasting
- Groundwater Quality Forecasting
- Plant Disease Inspection Using Deep Learning
- Anomaly Detection In Agriculture Data
The Final Verdict
In conclusion, agriculture forecasting tools like Odyx yHat used to forecast agriculture data in different formats. The data generated in smart farming can be molded into insights but it requires some sort of data transformation. So, companies need to hire data engineering, and data scientists on-premises to extract the beauty of data. To bypass these extraneous processes, the agriculture forecasting tool Odyx yHat has made the process cheap, straightforward, and easy to operate for non-technical executives.
