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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.

Global food security is critically threatened by plant diseases, which can be especially catastrophic for smallholder farmers whose economic survival hinges on a productive groundnut crop. It is therefore vital to engineer an algorithm capable of early, automated plant disease diagnosis. Working alongside a pathologist, a large-scale dataset of groundnut leaf imagery was assembled to enable this automated detection. In image classification tasks, Convolutional Neural Networks have demonstrated remarkable efficiency. This investigation constructs a disease identification and classification system using a tri-CNN architecture that incorporates DenseNet169, Inception, and Xception, all of which were initially trained on the ImageNet database. The proposed ensemble strategy formulates final predictions for test data by applying two custom nonlinear functions to the decision outputs of these base learners, integrating their scores across four standard performance measures: recall, precision, accuracy, and F1-score. The tailored CNN models are thus utilized to train our system for superior results. The methodology was tested on a real-world groundnut leaf dataset. The introduced model registered an accuracy reading of 98.46%.