Application of Remote Sensing Technology in Orchard Precision Management

Dear Colleagues,

To achieve effective orchard management, increase yield stability and gain economic benefits, managers must rely on accurate and timely information regarding an orchard’s environment, fruit physiology, and biochemistry to make informed decisions about planting, irrigation, fertilization, pest control and other related matters. Traditionally, orchard planting information has been primarily obtained through on-site observation, planting records, manual sampling, and laboratory sample testing. However, these methods are time-consuming and labor-intensive, with high costs for large-scale implementation. As a result, they cannot meet the requirements for real-time monitoring and the rapid management of orchard trees over large areas.

After decades of development, remote sensing technology has made significant advancements in the temporal, spatial, spectral, and radiation resolution of data acquisition. It has become an innovative and cost-effective means of identifying and monitoring fruit forests as well as managing orchard planting.

With the seamless integration of remote sensing technologies, including unmanned aerial vehicles and satellite imagery; and emerging tools such as global navigation satellite systems, geographic information systems, big data analytics, and artificial intelligence, precision agriculture is gradually achieving its goal of continuously optimizing operations to increase output while reducing input costs and minimizing losses. Several of these technologies have been implemented in orchard production, ushering in an era of precision management for orchard planting.

This Special Issue welcomes contributions from scholars reporting current achievements and frontier findings that identify the future direction of these cutting-edge applications in orchard planting management. This Special Issue will publish papers dealing with the following main aspects:

• Remote sensing survey of basic orchard information, e.g., investigation of orchard distribution, orchard age survey, and orchard infrastructure distribution survey.
• Remote sensing inversion of orchard parameters, including but not limited to the inversion of physiological and biochemical parameters (e.g., LAI, chlorophyll contents of orchard trees, and nutrient content) of orchard trees, and the inversion of environmental parameters (e.g., canopy temperature, soil moisture and nutrients, photosynthetically active radiation).
• Orchard planting management, e.g., orchard tree growth monitoring (including tree height, crown width, leaf nutrient content, leaf green content, number of flowerings, and number of fruit), Orchard tree water management (vegetation evapotranspiration, water stress pattern), orchard tree nutrient (e.g., nitrogen, phosphorus, potassium) management, and pest management.

We particularly encourage the submission of papers giving experimental evidence of the integration of disciplines such as engineering, mathematics/statistics/physics, and automation. Contributions focusing on agricultural resources management; artificial intelligence; decision support systems; and technologies and solutions for the precision management of orchards, digital and smart agriculture, orchard buildings, and facilities are welcome.

Dr. Xinyang Yu
Dr. Long Li
Guest Editors

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