DJI Agriculture is a division of DJI focused on providing precision agriculture solutions using drones. The company offers a range of products and services, including drones, software, and technical support. Its solutions aim to enhance productivity, efficiency, and sustainability in agriculture, enabling farmers to optimize resource use and maximize yields. The DJI Agriculture website provides information about its products, services, and initiatives, including case studies and relevant industry news. https://ag.dji.com/

Crop Yield Estimation and Prediction" describes remote sensing technologies and crop growth modeling to estimate and predict crop production. The document addresses the historical development of such technologies as well as emerging trends in the field. Remote sensing platforms, such as satellites, aircraft, and ground-based equipment, play a crucial role in collecting crop data, which can be used to feed crop growth models. The integration of data assimilation methods, such as the Kalman filter, is essential for combining remote sensing data with field information to enhance the accuracy of crop yield estimation and prediction. The text also highlights the challenges associated with using remote sensing technologies and crop growth modeling, such as uncertainties in weather and soil conditions and the potential for measurement errors

GUSS is an autonomous spraying system designed to enhance efficiency, precision, and safety in agriculture. The system allows a single operator to control up to eight GUSS sprayers simultaneously using a laptop in a work vehicle. GUSS provides a solution to labor challenges in agriculture, enabling farmers to complete tasks more quickly, with greater accuracy, and with reduced risks to workers. Additionally, GUSS lowers costs, eliminates operator errors, and boosts productivity.

https://gussag.com/

The concept of precision agriculture, focusing on two main methods: sensor-based and map-based. The first method uses real-time sensors to collect data and adjust applications during the process, while the second relies on pre-generated maps to guide variable applications. The final text discusses the integration of the two methods, demonstrating how combining historical and real-time data can optimize decision-making, resource utilization, and the effectiveness of agricultural practices.

A recent article discusses the role of artificial intelligence (AI) in agriculture, exploring its benefits, applications, challenges, and future. It addresses how AI can help farmers make more informed decisions, optimize resource use, automate tasks, and improve productivity. Additionally, the article examines the impact of generative AI in agriculture, highlighting its potential in scenario simulation and optimization of agricultural practices. Finally, the article describes the challenges of AI adoption in agriculture, such as high initial costs and resistance to technological change.

https://autogpt.net/what-you-should-know-about-ai-in-agriculture/

Today we discuss the concept of controlled traffic farming (CTF), an agricultural traffic management system aimed at minimizing soil compaction and increasing productivity. CTF is a traffic management system that uses permanent traffic lanes, typically in a 3–3.1-meter grid, to ensure that all agricultural equipment moves within these lanes, thereby minimizing soil compaction. The text examines the principles of CTF, its advantages and disadvantages, and how the technology is being adopted in different parts of the world. It also addresses the impacts of CTF on crop yield, energy use, and sustainability