Nowadays, a large amount of data is collected, 328.77 million terabytes of data are created every day, according to the latest estimates, and that equates to 120 zettabytes per year [1, 2]. However, data is a collection of discrete objective facts about an event or process that themselves are of little use unless they are converted into information and knowledge. Four dimensions of growth in data are volume, variety, velocity, and veracity.

Data Mining is a fertile area of research and can be applied to almost any area where large amounts of data need to be stored and processed, such as agricultural fields. Data mining is the use of data analysis tools to discover and extract useful and previously unknown patterns and valid relationships in large datasets. The extracted information is typically represented as a model of the semantic structure of the dataset, where the model can be applied to new data for the two main purposes of data mining: description and prediction.

Description focuses on finding human-interpretable patterns describing the data and prediction involving using some variables or fields in the database to predict unknown or future values of other variables of interest. The use of data mining and analysis for predictive analytics in agriculture prediction is the focus of this article.

What is Predictive Analytics ?

Predictive analytics is a branch of advanced analytics that uses data mining techniques to enable us to predict upcoming challenges and customer interests by analyzing current and historical data and using statistics, data mining, machine learning, and artificial intelligence techniques to make the most appropriate decisions. Wherever we work with large amounts of data, we use predictive analytics to proactively predict trends and behaviors based on data.

Predictive analytics can be categorized into three types :

1. Predictive models that perform predictive analysis mainly help in obtaining future predictions by analyzing various statistical data. This model evaluates the probability that similar units will exhibit a certain performance in different samples.

2. Descriptive analysis helps to find relationships between external factors within a company.

3. Decision modeling provides critical support for making the most appropriate decisions based on future factors and their impact on the business.

In general, predictive analysis involve several steps [3]:

• Requirement collection: determining what prediction the client wants,

• Data collection: gathering the required data,

• Data analysis and messaging: converting data into structured form,

• Statistics and Machine learning: applying statistics and machine learning techniques,

• Predictive modeling: developing a model for predictions,

• Prediction and monitoring: making predictions and monitoring the model.

Predictive Analytics Applications

Some of the application areas of predictive analytics are predictive analytics in agriculture, banking and financial services, retail, health and insurance, oil gas and utilities, government and public sector. A few of the foremost common applications of predictive analysis can be recorded as:

• Resourcing Materials: Many businesses worldwide are facing critical resource scarcity issues due to unexpected disruptions. Predictive analytics can help identify upcoming challenges. There are risks associated with future resource availability and potential suturing troubles. Predicting these risks and challenges ahead of time can help in making strategic decisions to mitigate them.

• Supply Chain Operations: Predictive analytics in business can help enterprises develop suitable supply chain management strategies for the international market by anticipating potential issues in managing supply chain operations.

• Product Designing and Manufacturing Area: The use of predictive analytics can enhance a company’s product design and manufacturing tasks in the global market. Predictive analytics can assist in understanding customer preferences and purchase decisions, enabling informed decisions in product design and manufacturing. Planning is crucial in product design and manufacturing, particularly in areas that are closely tied to consumer demand and interest. This can lead to increased business growth.

• Marketing and Promotional Applications: The marketing and promotional operations of a company provide an opportunity for the enterprise to enhance its brand awareness and share information about its products or services with consumers. Analyzing consumer feedback through predictive analytics can enhance a company’s marketing strategy, leading to increased consumer base, sales, and revenue in a competitive market.

Predictive Analytics in Agriculture

The agricultural industry faces a critical challenge due to the increasing rate of environmental and political factors. Climate change and natural calamities pose a serious threat to agricultural activities. Predictive analytics can help predict upcoming threats and provide solutions to mitigate these challenges. The use of predictive analytics in agriculture can simplify decision-making and strategic development by anticipating and managing potential risks. The use of predictive analytics in agriculture can simplify decision-making and strategic development by anticipating and managing potential risks. This technology can also improve manufacturing, human resource management, farmer relations, supplier communication, and overall efficiency in the global agriculture sector. The significant impact of predictive analytics on agriculture is evident.

Predictive analytics can assist in evaluating upcoming risk factors in business and developing strategies to address future challenges in the agricultural industry. In agriculture, predictive analytics can help explore new markets and expand into new territories, leading to increased revenue for a company. The use of predictive analytics can enhance various agricultural activities, including farming, product storage, and risk management.

Tools and Methods of Predictive Analytics in Agriculture

The application of predictive analytics in agriculture has revolutionized the way farmers manage their operations, leading to enhanced crop productivity, resource optimization, and risk mitigation. Some of the companies that have provided analytical tools are listed below:

• AgroScout offers a range of tools and services in the field of agriculture, leveraging AI and data-led precision agriculture to provide real-time insights into crop management.

• Sairone offers an all-in-one solution for drone-based crop monitoring in the field of agriculture. Their platform can provide services such as Crop Yield Prediction and Pest and Disease detection using data mining and machine learning. Using Saiwa platform you can easily have access to all these privileges.

• SpaceAg offers a digital notebook for agriculture called SpaceAG Forms, which is designed to help farmers collect, organize, and analyze data from their farms.

• Solvi is a product monitoring platform. They provide various tools and features, including AI-based analytics, to help farmers make better decisions about their crops.

Conclusion

Predictive analytics is a powerful tool that can be used to improve decision-making in all industries, including agriculture. By analyzing historical and current data, predictive analytics can help farmers identify potential risks and opportunities, improve resource utilization, and gain a competitive advantage.

Some of the specific benefits of predictive analytics in agriculture include:

• Reduced risk

• Increased revenue opportunities

• Improved production quality

• Higher customer satisfaction

• Improved asset utilization

There are a number of companies that offer predictive analytics tools and services for the agriculture industry. Some of the most popular include AgroScout, Sairone, SpaceAg, and Solvi.

With the increasing rate of environmental and political challenges facing the agricultural industry, predictive analytics is becoming more important than ever. By leveraging this powerful technology, farmers can better manage their operations and ensure the long-term sustainability of their businesses.

References:

[1] https://explodingtopics.com/blog/data-generated-per-day

[2] https://cloudscene.com/region/datacenters-in-asia-pacific

[3] Kumar, Vaibhav, and M. L. Garg. “Predictive analytics: a review of trends and techniques.” International Journal of Computer Applications 182.1 (2018): 31-37.