The identification of tomato maturity is significant to extend the fruit shelf life and generate the scientific processing strategy. Tomato maturation is a gradual process, and the internal physicochemical characteristics are most related to maturity states. Merely choosing visual features to identify maturity would cause discriminant errors. This study designed a simple and effective identification method for tomato maturity by integrating color moments and physicochemical indices. The color moments were extracted by an adaptive K-means clustering image processing program, and firmness, soluble solid content and sensory evaluation were measured by professional techniques. The optimal multidimensional index set was formulated according to color moments and physicochemical indices simultaneously. To reduce the confusion between adjacent stages, a novel multinomial logistic regression with kernel clustering (MLRKC) method was designed to identify maturity, and the accuracy was 95.83% for tomato testing set. Moreover, the traditional image features set and some classic methods were applied to verify the performance of proposed method, respectively. Finally, the proposed method was applied to identify the tomatoes in the realistic circumstance. The identification results demonstrated satisfactory performances and promising applications of MLRKC method integrating color moments and physicochemical indices. Practical Applications Tomato is a climacteric fruit which could mature after harvesting. Identification tomato maturity stage is significant to decide the optimal transportation modes, inventory strategies and processing technology. Traditional methods for identifying tomato maturity were high-cost and complicated, which were inefficient for small-scale production. The method proposed in this study could simply the identification steps and reduce the operating cost, and also provide more accurate and valuable information. The investigated theoretical basis could be incorporated into the small farmers and small-scale food processing companies to achieve tomato precision processing with low additional costs. 1 | INTRODUCTION Tomato is rich in nutrients such as vitamins, carotenes, dietary fiber, and is an essential part of human daily diets (Feng, Zhang, Adhikari, & Guo, 2019). As one of the climacteric fruits, tomato has the postharvest maturity characteristics, that is, maturing after harvesting

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Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

Fruit maturity is an essential factor for fresh retailers to make economical distribution scheduling and scientific market strategies. In the context of farm-to-door mode, the fresh retailers could incorporate the postharvest maturity time, picking time and distribution time to deliver high-quality fruits to consumers. This study selects climacteric tomato fruits and formulates a postharvest maturity model by capturing the firmness and soluble solid content (SSC) data during maturing. A joint picking and distribution model is proposed to ensure tomatoes could arrive at consumers within expected maturity time windows. To improve the feasibility of proposed model, an improved genetic algorithm (IGA) is designed to obtain solutions. The results demonstrate that the joint model could optimize the distribution routing to improve consumer satisfaction and reduce the order fulfillment costs. The proposed method provides precise guidance for tomato online retailers by taking advantage of postharvest maturity data, which is conducive to sustainable development of fresh e-ecommerce.