Signs of Change: The Future of digital media — Blog #1: Growing a Sustainable Future: Agriculture’s Rise of Data and Automation
Sustainable agriculture is now widely acknowledged as an essential solution to feeding a growing population while minimizing environmental impacts. Data and automation are crucial to this project because they transform farming practices and open the door to a more prosperous, productive, and environmentally friendly future. An excellent example of this development is the incorporation of data and automation in John Deere’s agricultural machinery.
Precision Farming: John Deere, a well-known agricultural equipment maker, is at the forefront of the sustainable agriculture movement (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). Precision farming is now possible thanks to high-tech machinery with data-driven technology and remains a massive opportunity for dealing with labour shortages, increasing yields to feed the growing world, reducing carbon emissions and more (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). Farmers may get precise data about crop health, weather patterns, and soil conditions using sensors, GPS systems, and machine learning algorithms (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). Using this information, they can enhance the usage of pesticides and fertilizers, use less water, and generate less waste, which will increase yields and have a less harmful effect on the environment (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019).
Robotics and automation: Automation technologies are changing agriculture by streamlining labour-intensive processes and reducing resource waste (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). John Deere’s technology incorporates automation, autonomous machine operations like planting, spraying, and harvest possible. Automated systems can run continuously and precisely, maximizing resource allocation and reducing the likelihood of human error (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). Farmers can focus on more worthwhile endeavours because doing so boosts productivity and reduces the need for labour from people (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019).
Data and automation will still impact sustainable agriculture in the future (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). Modern machine learning algorithms will enable predictive analytics to help farmers forecast production, disease outbreaks, and the best harvest times (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). By incorporating Internet of Things (IoT) devices, data collection and analysis will be improved, facilitating better decision-making (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). Robotics and drones will also be increasingly important for pollination, weed control, and agricultural monitoring (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019).
As John Deere’s efforts demonstrated, integrating data and technology in agriculture considerably increases the likelihood of a sustainable future (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). Using technology, farmers may improve resource efficiency, reduce environmental damage, and ensure food security (Pucci, 2019; Kolipaka, 2020; Ferhan et al., 2022; Sharma & V, 2019). It is essential to address data privacy, access, and the digital divide to ensure these game-changing technologies are used equally. We can create a more stable and sustainable agriculture system by implementing automated and data-driven solutions.
Works Cited:
References:
John Deere streamlines precision ag capabilities. (2015). Delta Farm Press, 16–.
United States Precision Farming Market (2015).–2019 with Ag Leader Technology, AgJunction, John Deere, Dickey-John, Teejet Technologies & Trimble Navigation Dominating. (2015). PR Newswire Association LLC.
Pucci. (2019). In-Cab Systems, Transformed from the Dawn of Precision Ag. CropLife, 182(6), 24–32.
Kolipaka. (2020). Predictive analytics using cross-media features in precision farming. International Journal of Speech Technology, 23(1), 57–69. https://doi.org/10.1007/s10772-020-09669-z
Ferehan, Haqiq, A., & Ahmad, M. W. (2022). Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics. Journal of Food Quality, 2022, 1–8. https://doi.org/10.1155/2022/7484088
Precision Farming and Predictive Analytics in Agriculture Context. (2019). International Journal of Engineering and Advanced Technology, 9(1S5), 74–80. https://doi.org/10.35940/ijeat.A1023.1291S52019
Sharma, & V, V. (2019). Predictive Analytics In Weather Forecasting Using Machine Learning Algorithms. EAI Endorsed Transactions on Cloud Systems, 5(14), 159405–. https://doi.org/10.4108/eai.7-12-2018.159405
https://www.deere.com/en/technology-products/precision-ag-technology/
