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A shortage of calcium and the resulting physiological problems, like tip burn, create serious difficulties for indoor hydroponic lettuce growing, reducing both harvest and grade. This paper puts forward a unique strategy that merges cutting-edge image segmentation and classification methods to spot calcium insufficiency in lettuce during its growth phases under colored LED light. Recognizing nutrient shortfalls at an early stage is key for prompt correction and efficient nutrient control. The trial cultivated butterhead lettuce in a Deep-Water Culture (DWC) arrangement with managed calcium doses. Image preparation steps, involving Contrast Limited Adaptive Histogram Equalization (CLAHE) and Red Color Correction (RCC), were used to boost picture quality and uniformity, and augmentation was performed to increase model resilience. Our method adopted a two-step workflow that enabled us to exploit the advantages of dedicated models. First, lettuce foliage was separated from the backdrop through state-of-the-art architectures, including U-Net, U-Net++, Recurrent U-Net, and Inception U-Net. U-Net++ showed the top segmentation performance (98.56%) with strong generalization compared to its counterparts. Segmentation extracts the target zone and discards background clutter, permitting the classifier to focus more productively on disease-indicating traits. Deep learning classification networks, namely ResNet and EfficientNet, were used in the second step to identify calcium insufficiency from the segmented pictures. EfficientNetB2 stood out as the most dependable classifier, securing an accuracy of 91.51% on the RCC dataset, while Resnet50 attained a closely matching accuracy of 91.18% on that same dataset. This work emphasizes the promise of weaving deep learning models into automated hydroponic setups for live nutrient tracking, supplying a workable answer to improve output and sustainability in indoor hydroponic operations.

Grass carp hemorrhagic disease (GCHD), which is induced by grass carp reovirus (GCRV), stands as one of the most severe communicable diseases jeopardizing freshwater aquaculture, creating a demand for innovative, effective, and affordable surveillance tools. Machine vision can serve as a powerful inspection method, enabling early alerts for GCHD epidemic spread. Yet, experimental dataset tests indicate that the detection precision of existing deep learning algorithms remains unsatisfactory. In the present study, image datasets of the model organism Chinese rare minnow (Gobiocypris rarus) manifesting GCHD signs were gathered, and a self-attention-based convolutional neural network (SA-CNN) deep learning architecture was designed. This SA-CNN framework is capable of reliably pinpointing Chinese rare minnow individuals displaying GCHD symptoms. Through pre-processing the extracted imagery using refined k-means and improved watershed algorithms, tightly clustered or superimposed fish can be adaptively disentangled within populations, thereby elevating recognition accuracy. With an optimized loss function, the SA-CNN model delivers an accuracy of 99.1%, a recall of 97.7%, and an F1 score of 96.6% on the collected datasets of symptomatic fish. Its precision outperforms current state-of-the-art you only look once (YOLO) network families by margins between 4.9% and 25.6%, varying across different model variants. Image identification demands a mere 95ms per frame, highlighting considerable operational efficiency. Ablation studies confirmed that incorporating the self-attention module yielded accuracy gains of 3.8% and recall improvements of 3.0%. Accordingly, this refined SA-CNN framework furnishes an efficacious solution for the essential large-scale deployment of GCHD surveillance initiatives tracking GCRV transmission, paving the way toward intelligent, health-oriented, and precision aquaculture.

The present investigation tackles several pressing difficulties in modern poultry operations, namely pathogen transmission, workforce deficits, inadequate surveillance capacity, and intensifying requirements for superior animal welfare standards. To meet these demands, an elaborate machine vision framework based on deep learning is put forward, wherein the YOLOv11 algorithm is deployed to autonomously observe and administer poultry. A collection of 330 chicken photographs was gathered from Thai agricultural facilities under a range of environmental circumstances and subsequently increased to 1,716 images by means of rotation, along with alterations to brightness and saturation parameters. Training of the YOLOv11 architecture was carried out on the NVIDIA Jetson Orin Nano Developer Kit, with performance gauged through precision, recall, and mean average precision (mAP) metrics. The architecture attained remarkably high fidelity, registering precision of 0.964, recall of 0.938, and mAP of 0.963, correspondingly. The fusion of optical with thermal imaging permitted the apparatus to circumvent limitations imposed by variable illumination and ambient settings. Outcomes from practical deployment evidenced real-time observation and tracing of numerous birds, thereby supporting disease mitigation, feed optimization, and holistic farm governance. This inquiry advances the domain of intelligent agriculture by furnishing a wholly automated, expandable, and exceedingly accurate poultry oversight infrastructure designed to foster ecological responsibility, operational productivity, and elevated animal welfare within contemporary poultry cultivation.