Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards—State of the Art, Challenges, Data Fusion and Integration

The digital transformation of horticultural production systems is well underway, driven by the pressing need to increase productivity, improve resource use efficiency, and adapt to climate change. In orchards and vineyards, the Agriculture 4.0 revolution is promoting a fast-paced development and application of innovative sensor technologies and data-driven decision-making systems for improved management and business performance [1,2]. These tools, merged with the fast advances of IoT and AI are enabling researchers and growers to monitor plant physiological status, estimate yield, improve traceability, and inform precision management strategies with unprecedented spatial and temporal resolution [3,4].

This Special Issue brings together nine original research papers (referred to as contributions) that illustrate the breadth of approaches currently being pursued in the domain of sensor-based horticultural monitoring. The contributions span a diverse range of crops (including apple, grapevine, mango, cherry and olive), sensor types (from LiDAR to RGB, thermal, hyperspectral and microtensiometers), and data integration strategies—from on-tree fruit measurement to full orchard-level digital ecosystems.

Several papers in this Special Issue reflect new applications of vision and imaging systems to estimate fruit size, yield, and vegetative status. Gonzalez Nieto et al. [contribution 1] evaluated two commercial computer vision systems across 23 apple orchards to quantify trunk cross-sectional area, flower cluster number, thinning efficacy and final yield. While both systems demonstrated promising correlations with manual measurements, they also revealed current limitations in accuracy at high fruit loads, providing critical insights for future development and implementation. Similarly, Sun et al. [contribution 2] proposed a lightweight deep learning model (YOLOv5-PRE) for rapid and efficient apple detection under variable lighting conditions in complex orchard environments. The study highlights the gains in both speed and precision made possible by integrating attention mechanisms and lightweight network architectures.

Mango was the focus of two complementary papers that address in-orchard sizing and yield prediction. In part one, Neupane et al. [contribution 3] compared three machine vision approaches using RGB-D cameras for automated on-the-go fruit sizing. Their work demonstrated the potential for on-tree video imaging to generate fruit weight distributions relevant for packing and marketing. In part two, Amaral and Walsh [contribution 4] used manual calliper measurements to develop forward models of mango fruit mass at harvest, establishing prediction windows based on growing degree days and validating the robustness of their linear growth model across cultivars and environments. Together, these studies offer an integrated methodology for linking vision-based field measurements to postharvest size distribution and improved harvest and market logistics.

Sensor-based approaches to water stress detection and irrigation scheduling are another key theme in this Special Issue. These are critical for improved water management under increasingly limited water supply and uncertainty from variable climate challenges. Khosravi et al. [contribution 5] deployed fruit gauges in four olive cultivars to monitor real-time fruit growth under deficit irrigation, calculating metrics such as daily growth rate and hysteresis indices to infer cultivar-specific responses to water availability. Meanwhile, Lakso et al. [contribution 6] tested a novel microtensiometer embedded directly into tree stems for continuous measurement of water potential in orchards and vineyards. Their findings underscore the importance—and technical challenges—of obtaining long-term, stable plant water status data under field conditions, particularly for crops with complex xylem anatomy.

Thermal and LiDAR-based methods for ecophysiological monitoring were explored by Tapia-Zapata et al. [contribution 7], who fused thermal images with LiDAR-derived point clouds to model surface wetness on sweet cherry fruit. By integrating temperature profiles with dew point thresholds, the study provided a new approach to predict conditions associated with fruit cracking—an important disorder in cherry production. This work also highlights the potential of high-resolution, spatially explicit microclimate data for understanding physiological responses in orchard systems.

Beyond sensors alone, the need for structured data integration frameworks was a prominent concern. Williams et al. [contribution 8] addressed this by presenting a novel digital data ecosystem for orchard research, implemented at the Tatura SmartFarm. Their service-oriented platform supports seamless storage, analysis and interoperability of diverse datasets, laying the groundwork for early-stage fruit traceability and multi-sensor fusion. This contribution responds to a growing need for robust, flexible data infrastructure as the volume and variety of sensor data continues to expand.

Finally, Bodor-Pesti et al. [contribution 9] investigated how RGB-derived vegetation indices correlate with grapevine leaf chlorophyll content, providing a potential method for non-destructive assessment of canopy physiological status. Their findings confirmed significant correlations between various RGB indices and chlorophyll concentration, suggesting future applicability of on-the-go RGB sensors for monitoring vine health and response to stress.

Taken together, the studies in this Special Issue reflect the rapid evolution and growing maturity of sensor and data integration technologies in perennial horticulture. They also illustrate the challenges that remain—particularly in scaling technologies from experimental to commercial application, ensuring accuracy and robustness in diverse orchard environments, and integrating heterogeneous data streams into actionable insights. Some challenges and opportunities of within-orchard technology applications were previously reviewed [5,6]. Technologies like those described in this special issue will become more widely used in orchard systems that are simplified and robot-ready like in narrow orchard systems [7].

The authors have provided high-quality contributions, complemented by the reviewers’ constructive feedback and dedication to scientific rigour. This Special Issue provides valuable reference points for researchers, technologists, and practitioners working to advance sensor-based monitoring and management of orchards and vineyards. The integration of diverse sensing modalities—from plant-based and proximal to remote plant-based systems—will be essential to realise the full potential of precision horticulture in a changing world.