How Data Analytics is Transforming the Agriculture Industry?
Agriculture has come a long way from its traditional roots from the past. As an industry, it has evolved from a stage where it relied solely on recommendations from fellow farmers to a modern, data driven endeavor. Nowadays farmers are able to harness insights backed with loads of historical data to come to a conclusive analysis on the crop to be planted and the cultivation method to be used.
Data Analytics is now penetrating age-old agri-processes to streamline irregularities and increase efficiency in cultivation, irrigation, harvesting, supply chain management, and logistics to ensure that there is little to no risk involved when dealing with perishable goods.
The scope of Big Data Analytics in Agriculture lifecycle
IoT, BigData, and Cloud computing are revolutionizing the way agriculture functions as an Industry in India and around the world. Data Analysis in agriculture globally is valued currently at 565 million USD, and the projected valuation by 2023 is 1256 million USD.
Data Analysis in agriculture is being leveraged to optimize every step in the agriculture lifecycle to be more cost-effective and efficient. From crop selection, cultivation method, harvesting, and supply chain management, the impact is being felt at every stage of the value chain.
With sensors and connected devices interacting with each other on the farm, farm owners and managers are now equipped with volumes of crop data in real time to guide farmer’s actions. Big data in agriculture is transforming livestock care, developing efficient risk assessment modules, democratizing the potential of urban farming, and catalyzing efficient use of resources (land and labor).
Here are some of the major benefits of applying data mining and machine learning techniques in agriculture.
Improved crop management:
With insightful crop data, farmers can make informed decisions on the type of crop to grow, choose a strain that is best suited for the atmospheric conditions, rain seasons, and type of soil to make a profitable harvest. Hybrid varieties or breeds that are most suited to the soil and climatic conditions can be recommended based on data analysis that are most resistant to disease and spoilage.
Better risk assessment:
Risk in the agriculture sector is inevitable, but the ability to predict and manage the risk at every stage of the lifecycle makes the farmer better equipped to take tactical decisions. Big Data and Cloud computing utilizes data from Google Earth, global weather conditions, and data fed in by the farmer to project a roadmap that helps farmers plan the journey right from crop selection to distribution. It also factors in the local market prices, natural calamities, pest infestation etc. that might increase or decrease the value of commodity and hardships that a farmer could face with respect to supply chain management. Data aids farmers in making decisions that could help them sway away from potential high-risk scenarios in the lifecycle of the crop.
Efficiencies in supply chain
Supply chain management is not just about distributing the finished goods to the desired market anymore. With data analytics, farmers are now empowered with insights that can help them predict the market conditions, consumer behavior towards the finished goods, factor-in inflation, and other variables that will help them plan the entire process even before sowing the seeds. This becomes a salient insight as it allows farmers to manage the conditions that enable them to maximize return on investment and mitigate any unnecessary loss.
The Bottom line
Big data is giving more power and control to farmers and greater visibility on their finances. It helps streamlining and saving time in the process from farm to table especially for perishable produce with a small shelf life. This means better tasting and fresher produce for customers and better profits for the farmers themselves.
With agricultural management software, farmers can now innovate and venture into more efficient farming models, diversify their resources by exploring Aquaponics, greenhouse farming, and urban farming, among others. Such data led farming innovations are leading to farming habits that are sustainable and beneficial to local ecosystems.