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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.

An efficient pipeline for sorting clusters of touching corn kernels—potentially containing sound grains, defective grains, and foreign matter—is introduced. The method operates in two steps. First, a next-generation instance segmentation network, trained entirely on a corpus of synthetically generated images, separates the input scene into distinct individual objects. Second, a purpose-built, lightweight CNN architecture assigns each segmented object to one of three categories (sound, defective, or foreign matter). The segmentation model is learned using a strategy that eliminates the need for labor‑intensive, subjective manual annotation. For the classification step, the custom network is deliberately constructed with a minimal number of layer blocks, yielding a compact architecture suitable for on-device deployment. Experimental evaluations and comparative benchmarks confirm gains in both recognition accuracy and processing speed. The proposed segmentation and classification framework can be readily transferred to other cereal grains.