Advances in machine learning and artificial intelligence have led to many promising solutions for challenging issues in agriculture. One of the remaining challenges is to develop practical applications, such as an automatic sorting system for after-ripening crops such as tomatoes, according to ripeness stages in the post-harvesting process. This paper proposes a novel method for detecting tomato ripeness by utilizing multiple streams of convolutional neural network (ConvNet) and their stochastic decision fusion (SDF) methodology. We have named the overall pipeline as SDF-ConvNets. The SDF-ConvNets can correctly detect the tomato ripeness by following consecutive phases: (1) an initial tomato ripeness detection for multi-view images based on the deep learning model, and (2) stochastic decision fusion of those initial results to obtain the final classification result. To train and validate the proposed method, we built a large-scale image dataset collected from a total of 2712 tomato samples according to five continuous ripeness stages. Five-fold cross-validation was used for a reliable evaluation of the performance of the proposed method. The experimental results indicate that the average accuracy for detecting the five ripeness stages of tomato samples reached 96%. In addition, we found that the proposed decision fusion phase contributed to the improvement of the accuracy of the tomato ripeness detection.

Gray mold (Botrytis cinerea) is a fungal plant pathogen causing postharvest decay in strawberry fruit. Here, we conducted a comparative transcriptome analysis to identify differences in gene expression between the immature-green (IG) and mature-red (MR) stages of the “Sunnyberry” (gray mold-resistant) and “Kingsberry” (gray mold susceptible) strawberry cultivars. Most of the genes involved in lignin and alkane-type wax biosynthesis were relatively upregulated in “Sunnyberry”. However, pathogenesis-related proteins encoding R- and antioxidant-related genes were comparatively upregulated in “Kingsberry”. Analysis of gene expression and physiological traits in the presence and absence of B. cinerea inoculation revealed that the defense response patterns significantly differed between IG and MR rather than the cultivars. “Kingsberry” showed higher antioxidant induction at IG and upregulated hemicellulose-strengthening and R genes at MR. Hence, “Sunnyberry” and “Kingsberry” differed mainly in terms of the expression levels of the genes forming cuticle, wax, and lignin and controlling the defense responses. These discrepancies might explain the relative difference between these strawberry cultivars in terms of their postharvest responses to B. cinerea.

Optimum postharvest storage conditions increase the postharvest quality and shelf life of horticultural crops. The effects of forced-air precooling (FAP) and modified atmosphere packaging (MAP) on shelf life, physicochemical quality, and health-promoting properties of bell pepper (Capsicum annuum L. cv. Nagano) harvested at 90 and 50% coloring stages in May and July respectively, stored at 11 °C, 95% relative humidity were assessed. Fruits were subjected to four treatments: FAP + 30 μm polyethylene liner (FOLO); FAP-only (FOLX); 30 μm polyethylene liner-only (FXLO); and control (FXLX). The quality attributes, viz. weight loss, firmness, color, soluble solids content (SSC), soluble sugars, total phenolic content (TPC), total flavonoid content (TFC), 2,2-dephenyl-1-picrylhydrazyl (DPPH), and 2,2′-azino-bis-3-ethylbenzo-thiazoline-6-sulfonic acid (ABTS) were evaluated. The investigated parameters differed significantly (p < 0.05) among treatments except for soluble sugars. FOLO maintained sensory quality (weight loss, firmness, and color), physicochemical (SSC and soluble sugars), and health-promoting properties compared to other treatments during storage. The 50% coloring fruits had a huge variation between treatments than 90% coloring. The results revealed more TPC and antioxidant capacity in the 50% than in the 90% coloring fruits. The study highlights the need to consider the ideal fruit coloring stage at harvest under the effect of FAP and MAP treatments in preserving bell pepper’s postharvest quality and shelf life.

Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness.

In the present investigation, we have devised a hyperspectral imaging (HSI) apparatus to assess the chemical characteristics and freshness of the yellow croaker (Larimichthys polyactis) throughout its storage period. This system operates within the shortwave infrared spectrum, specifically ranging from 900 to 1700 nm. A variety of spectral pre-processing techniques, including standard normal variate (SNV), multiple scatter correction, and Savitzky–Golay (SG) derivatives, were employed to augment the predictive accuracy of total volatile basic nitrogen (TVB-N)—which serves as a critical freshness parameter. Among the assessed methodologies, SG-1 pre-processing demonstrated superior predictive accuracy (R_p² = 0.8166). Furthermore, this investigation visualized freshness indicators as concentration images to elucidate the spatial distribution of TVB-N across the samples. These results indicate that HSI, in conjunction with chemometric analysis, constitutes an efficacious instrument for the surveillance of quality and safety in yellow croakers during its storage phase. Moreover, this methodology guarantees the freshness and safety of seafood products within the aquatic food sector.