The cultivation of nutritionally and economically important crops like tomato are often threatened by dry spells due to drought as these crops largely depend on an assured water supply. The magnitude and intensity of drought is predicted to intensify under climate change scenarios, particularly in semi-arid regions, where water is already a scarce resource. Hence, it is imperative to devise strategies to mitigate the adverse effects of drought on tomato through improvement in the plant’s efficiency to utilise the moisture in the growth medium. Since the root is the entry point for water, its intrinsic structure and functions play a crucial role in maintaining the soil–water–plant continuum during moisture deficit at the rhizosphere. Grafting offers a great opportunity to replace the root system of the cultivated tomato plants with that of wild species and hence provide a rapid solution to modulate root system architecture in contrast to the time-consuming conventional breeding approach. However, the success in developing the best graft combination of cultivated tomato and rootstock depends on the source of rootstock and selection methods. In this study, we used a high throughput phenomics facility to assess the efficiency of tomato, grafted on the rootstocks of different genetic backgrounds, at different levels of moisture in the soil. Rootstocks included tomato cultivars and the hybrids, derived from the crosses involving wild relatives, as donor parents. Among the rootstocks, an interspecific (Solanum lycopersicum × S. pennellii) derivative RF4A was highly efficient in terms of productive use of water. The RF4A rootstock-grafted plants were more conservative in water use with higher plant water status through relatively better stomatal regulation and hence were more efficient in generating greater biomass under water stress conditions. These plants could maintain a higher level of PSII efficiency, signifying better photosynthetic efficiency even under water stress. The distinct response of interspecific rootstock, RF4A, to water stress can be ascribed to the effective root system acquired from a wild parent (S. pennellii), and hence efficient water uptake. Overall, we demonstrated the efficient use of a phenomics platform and developed a protocol to identify promising rootstock–scion combinations of tomato for optimization of water use.

The growing demand for high-quality horticultural produce, particularly dragon fruit, has emphasized the need for efficient post-harvest quality assessment systems. Manual sorting methods remain prevalent in many agricultural regions, but they are time-consuming, inconsistent, and highly dependent on human expertise. These limitations result in reduced market value, increased post-harvest losses, and difficulty in meeting export standards. To address this gap, this paper introduces ADQS-YOLO, an automatic dragon fruit quality classification and sorting mechanism powered by a fine-tuned YOLOv8n object detection model. The system was trained using a standard benchmark dataset containing labeled images of fresh and defective dragon fruits. YOLOv8n is selected for its lightweight architecture and high inference speed, making it ideal for resource-constrained environments. The model was deployed on a Raspberry Pi 4B, enabling real-time, on-device decision-making without reliance on external servers or cloud infrastructure. A working prototype has been developed, featuring a conveyor belt and mechanical sorting unit that autonomously classifies and separates fruits into fresh and defective bins. The proposed system achieved a better quality and sorting classification accuracy, demonstrating high reliability in operational conditions. Thus, ADQS-YOLO presented a cost-effective, scalable, and portable solution for automating fruit sorting, particularly suited for small- to medium-scale post-harvest facilities.