Spoilage Sense: A WSN‐Boosted Intelligent Packaging System With Adapted GRU, MambaNet for Spoilage Prediction and Fresh Fruit Quality Management

As the world food waste is increasing to over 1.3 billion tons per year, with most of it attributed to undetected spoilage in the fresh produce industry, there is a dire need for intelligent, responsive and scalable packaging solutions in the fresh produce industry. This paper presents an integrated multimodal smart packaging framework that addresses some of the fundamental issues arising with the fruit quality monitoring process, such as real‐time compression of data, dynamic environmental adaptation, sensor node fault and scalability of distributed sensor networks. The system uses a Spoilage Disentanglement Network (SD‐Net) in which MambaNet is used only as the sequence encoder. The parts added to it are a four‐factor latent space, a memory module and a Predictive Anomaly Detection Unit (PADU). The latent space and the memory module isolate latent spoilage variables—microbial growth, gas response and temperature fluctuations—within the complex sensor signals and PADU models the time series with Gated Recurrent Units (GRUs), allowing spoilage to be alerted in time and correctly. It is combined with edge computing to perform low‐latency preprocessing in order to filter unimportant data and retain high‐value indicators. Federated learning supports the privacy of data and constant model improvement among the decentralized nodes. Moreover, RFID‐based traceability and packaging integrity are also used to strengthen packaging integrity throughout the supply chain. The system is also applied to synchronization and consistency in huge sensor deployment, where it is important and ensures that there is still reliable spoilage detection in the event of node failure or change in ambient conditions. The model is evaluated over 30 days in a hybrid simulation environment with two parts: In Part A, physical sensors on eight instrumented cartons in a laboratory‐like environment are used only to set the sensor noise, response time and sampling parameters; in Part B, virtual data streams from 32 units with injected synthetic anomalies provide all classification and anomaly‐detection results. The data incorporate environmental, gas, visual, microbial and RFID features, and the spoilage stages are simulated reference labels assigned by author‐defined rules. Comparative analysis against baseline models shows lower prediction error for the proposed SD‐Net + PADU framework than for the baselines evaluated, achieving an F1‐score of 0.90 and R 2 of 0.96.