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Litchi, a delicate and juicy fruit with a short shelf life, has a harvest period of fewer than 14 days each year. The emergence of intelligent agriculture has spurred the integration of vision-based, automated systems for litchi collection. Nevertheless, standard techniques frequently rely on laboratory-based, high-capacity computing machinery, which raises concerns about bulk, power consumption, and real-world operability in litchi groves. To surmount these hurdles, we put forward a real-time litchi identification strategy tailored for complex settings, leveraging handheld, energy-efficient edge computing devices. To begin with, imagery from litchi orchards was compiled to strengthen data generalizability. After that, a single-stage detector, YOLOx, anchored in a convolutional neural network (CNN), was developed to pinpoint litchi fruits in captured images accurately. To ease installation on handheld, energy-miserly edge devices, channel pruning, coupled with layer pruning, was used to shrink the final model, reducing both its bulk and parameter count. In addition, knowledge distillation was applied to further polish the network. Test outcomes revealed the suggested technique achieved 97.1% compression, resulting in a tight litchi detection model occupying just 6.9 MB, while maintaining a 99 frames per second (FPS) frame rate. The technique proved 1.8 times swifter than the untreated model. Hence, our method slots effortlessly into compact, low-computation automatic harvesting apparatus, ensuring instantaneous and precise litchi spotting right inside orchard landscapes.

Forecasting crop yields provides crucial intelligence for growers of high-value commodities, helping them organize and streamline harvest logistics. The bulk of current yield forecasting approaches rely on manual sampling and statistical procedures, which are intricate, labor-intensive, and time-consuming. Lately, advances in image processing and the broader integration of autonomous systems, such as unmanned aerial vehicles, have enabled swifter, more precise yield forecasting across diverse crops. This work puts forward a novel, rapid, efficient, and accurate image processing algorithm. The segmentation technique proved capable of distinguishing mature citrus trees from their natural surroundings within the CIE (Commission Internationale de l'Éclairage) Lab color space. The influence of the spacing between the camera and the target on resolution and detection accuracy was examined and discussed. Outcomes revealed that distances from 1.524 meters (5 feet) up to 2.134 meters (7 feet) yielded high detection accuracy. The count of correctly recognized fruit attained 91.69%, with an average processing duration of 1.1 s for each image across a set of 132 images. A correlation coefficient (R2) equal to 0.98 was secured between the citrus counting algorithm and counts generated through human visual assessment of 66 sample trees. A comparative review of the introduced technique indicated greater accuracy and robustness than other related research.