This study addresses a robotic scooping task for deformable plastic objects like wet soil, pastes, or plasticines. In this task, a robot arm holding a spoon scoops the object with an arbitrary target weight. Scooping deformable plastic objects presents a challenge in robot control. The robot needs to consider the object’s invisible physical properties, which can be varied due to extrinsic factors. For this reason, the same scooping action on the same objects can exhibit different scooping outcomes. We tackle this problem with tactile sensors. If a robot perceives the reactive force during the scooping action through the sensors, it will lead to more accurate weighing. We realize this concept with a scooped weight prediction neural network. We first train the network to predict scooped weights, where input is tactile and motion parameters. Then, we identify the optimal motion parameters from the tactile and target weights. To verify the proposed method, we performed experiments by comparing two approaches: our proposed method with tactile sensors and the baseline without them. We trained the scoop weight predictive model with 80 scooping motions by randomizing one of the parameters. Our method had 1.40, 1.40, 1.30, 1.75, and 1.64 g average errors in scooped weights with 3, 5, 7, 9, and 11 g target weights, respectively, demonstrating smaller errors for most target weights than the baseline. The proposed method is easy to implement and potentially available for various applications such as food industries, laboratory automation, and assistive feeding in everyday life.