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This paper presents NutriConv, a lightweight multitask convolutional neural network designed to simultaneously perform food classification and weight estimation from single-item food images. Trained on the institutionally validated PANCAKE dataset from the European Food Safety Authority, NutriConv combines classification and regression objectives within a unified architecture, optimized via a hybrid loss function. While its classification accuracy remains lower than that of specialized single-task models, NutriConv achieves competitive regression performance and offers a practical balance between both tasks. Its compact design enables deployment on resource-constrained platforms such as smartglasses and mobile health devices, expanding its usability in real-world dietary tracking scenarios. Extensive experiments confirm its robustness, including external validation on the Nutrition5K dataset, underscoring the model\u2019s generalizability. 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