The Power of Data Science in Planting

By 2050, the current 7.3 billion people on the planet are expected to increase to 9.3 billion. According to the Food and Agriculture Organization (FAO), agriculture must expand by 70% to meet anticipated demand ("The Future of Agriculture," 2016). In order to meet the demands of this expanding population, there is a pressing need to increase crop production while using the few available resources, such as land, water, and fertilizers.

The way farmers and other agricultural professionals make decisions is changing due to data science (Matthews, 2019). With the help of modern technology, it is now possible to gather information about the soil, water, and minerals present in farms and store it in a centralized system known as the Internet of Things (IoT). IoT stands for the concept of interconnecting related devices to the Internet so they can independently share and exchange data (Clark, 2016). To create a larger volume, such data can be combined with data from outside sources like satellites, weather stations, and even data from nearby farms. In the aggregate, data analytics can be used to gather data that farmers can use to optimize their farming.

This article explores the expanding application of data science in contemporary agriculture. It first describes the need for data science in agriculture, then discusses the opportunities and potential problems that may arise during implementation. 

Agriculture-Related Innovation-Spurring Factors

Since the beginning of human civilization as a society, agriculture has dominated. Rearing crops and animals has always been a labor-intensive task. Farming has undergone significant changes in practices, equipment, and machinery. Agriculture is still in the stage of continuous improvement after years of research and development to get it to this point.

Farmers' reliance on their intuition alone to make agricultural decisions is one of the factors fostering innovation (The Future of Agriculture, 2016). There is a danger because the farmer's error could result in no harvest at all for that season. As a result, the farmer must reduce this risk and make decisions cost-effectively.

Potential Applications of Data-Based Solutions

Farmers constantly balance a variety of factors while making agricultural decisions. They must plan what they will cultivate, where they will cultivate it, and when to raise various crops. The use of irrigation, fertilizers, and pesticides should then be decided. The timing of the harvest, reaping, and sending the goods to market come next. This type of farming is an arbitrary science, so getting every variable right for the most significant profit is critical.

Thankfully, in this day and age, farmers can use data to help them make difficult decisions. Farmers can gather information from a variety of sources and use data analytics to learn more about their farms and crops. (For a detailed explanation of data analytics techniques, refer to the data analytics course in Mumbai. )Data from sensors used inside the farm, such as those measuring soil nutrients, water content, air permeability, etc. (also known as localized data), can be combined with data from external sources, such as temperature and rainfall, or used independently to obtain various types of information. These data can be combined to evaluate and implement changes as needed continuously.

Problems with the Application of Data-Based Solutions

The agriculture industry's resistance to change is one of the main challenges in implementing data science. Farmers are very hesitant to alter their farming practices because doing so could cost them money if something goes wrong. Only large-scale farmers can afford the significant investment needed to switch to digital farming methods. In comparison to smallholder farmers, big businesses can generate returns relatively faster ("The Future of Agriculture," 2016). Small-scale, uneducated farmers, might need help to implement digital farming and might not be able to make sense of the data that has been given to them. The possibility that data-driven solutions will only benefit knowledgeable large-scale farmers is another issue with their implementation.

Results and Implications of Data Science for Agriculture

In the modern world, digital transformation in agriculture has led to many innovations. One of these initiatives is MyCrop, a real-time, intelligent, self-learning system that considers each farmer's location, crop data, and weather. It provides information, knowledge, and resources to smallholder farmers through the use of big data, machine learning, and smartphone technology.

Data science is being used at the TH Milk facility in Vietnam to control the quality of milk production in cows, each of which is equipped with an RFID chip. The milking procedure is automated with sensors in the suckers that can recognize inflammation in the cow's mammary glands. The machine will stop milking if it notices inflammation, and the cow will be marked and examined. Similar chips that track movement are affixed to each goat's legs in AfiMilk. The goat will be examined for illness if it remains motionless for an extended time or exhibits hazy sleeping habits. 

For more information on modern big data tools, join the best data science course in Mumbai and prepare for a lucrative career in this fascinating field.