There are clear signs of an imbalance in nature, including pollution, soil degradation, decline in wildlife population, and human interference with plants and animals. The human population will keep growing, putting more pressure on the agricultural ecosystem. Therefore, technology is a crucial factor in agriculture and sustainable development, both now and in the future.
Agricultural practices have undergone significant changes in recent years due to technological advancements. Using sensors, machines, and information technology has made modern farms more efficient and profitable. Advanced technologies such as robots, GPS, temperature and moisture sensors, aerial images and analytical tools, including crop yield prediction software are now commonly used in agriculture to improve precision and safety while protecting the environment.
The agriculture industry is seeing a consistent improvement in crop yields and income for farmers due to the ongoing use of digital and analytical tools. This trend is expected to continue and have a long-lasting impact.
Crop Yield Estimation
Predicting crop yield is essential for ensuring global food security. Still, it is a challenging task due to various factors that affect the yield, such as genotype, environment, management, and intricate interactions between them. There are three main categories of crop yield prediction methods: linear, machine learning, and crop models. Each of these methods has its strengths and limitations.
EOS Data Analytics is an experienced provider of satellite imagery analytics for agriculture and other industries. The company developed the platform for precision farming, EOSDA Crop Monitoring, to help growers and other stakeholders make effective data-based decisions.
EOSDA’s data scientists and engineers have created effective techniques for estimating crop yields through remote sensing and machine learning models. EO satellite data application helps to cover individual agricultural lands and entire regions.
To achieve the highest level of precision and productivity in predicting crop yields, EOSDA combines biophysical and statistical models. This fusion approach enables the company to tackle more intricate tasks. The quality of statistical data influences the accuracy of yield forecasting, ranging from 85% to 95%.
Crop yield estimation using remote sensing is crucial for making global, regional, and field agricultural decisions. The prediction relies on soil, weather, environment, and crop parameters. Decision support models commonly identify essential crop features for accurate forecasts.
By utilizing farm and in situ observations and existing databases from sensors connected to the Internet of Things, it is possible to predict yields using more straightforward statistical methods or decision support systems. Additionally, this data can be used with artificial intelligence to handle many parameters over time and space. Precision management tools and data collection capabilities can provide extensive databases for meteorology, technology, and information on soil and different plant species.
Read Also: Methods of Crops Propagation and Factors Affecting Crop Production
Crop Science Market
According to estimates, the worldwide crop protection market has grown by 4.7% in 2021 and is now worth $65,206 million. Herbicides are the most widely used crop protection products, accounting for approximately 44% of the market, followed by fungicides at around 27% and insecticides at roughly 25%. Biological crop protection products comprise about $3.3 billion of the overall market.
The seed market has also been growing during the last three years. Sales of genetically modified seeds increased by 11.6% in 2021. Of the GM seeds sold, 58% were stacked traits that contained genes for herbicide tolerance and insect resistance. Notably, CRISPR gene-editing systems have been identified as a significant agricultural advancement.
There is a growing interest in investing in sectors like biologicals and digital agriculture, which have a better environmental impact.
The COVID-19 pandemic had little impact on crop inputs since food production is essential. However, some challenges were faced, such as reduced availability of domestic and migrant farm labor, delays in shipments at ports, lower cotton consumption in the textile industry, reduced consumption of certain vegetables due to the closure of the hospitality sector, and decreased demand for biofuels.
GIS and Agriculture Science
GIS has great potential in agricultural planning and can help optimize crop production, manage soil sustainably, and protect crops from pests and diseases. The given technology can also be applied to crop yield forecasting methods. It can also address the challenges of adapting to climate change and meeting the needs of society.
Agricultural scientists collaborate with authorities to manage agricultural land, which involves analyzing soil, managing soil, examining crop yields, and considering population growth. With the changing climate, understanding the statistical analysis of crop yield changes in relation to CO2 levels is becoming increasingly important, and GIS can assist in this effort.
Read Also: Strategies for Reducing Water Pollution
