Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of (T670+T760-T640)/(T670+T760+T640) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index ((T670+T760-T640)/(T670+T760+T640)). Different indexes were used as the input of linear discriminate analysis (LDA) and of k-nearest neighbor (k-NN) algorithm to identify the maturity, and it was found that k-NN with (T670+T760-T640)/(T670+T760+T640) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.

The production and nature of the biocrude obtained from Spirulina sp. by hydrothermal liquefaction (HTL) technology is focused in this investigation. Our aim is to evaluate the interaction of different factors on the bio-oil production through HTL using microalgae that contains relatively low lipid content and high protein. Optimization of three key parameters—concentration (mass of algae per mass of solvent), reaction temperature, and holding time—was carried out by response surface methodology (RSM). In this work, we used central composite design to conduct the experiment process. Graphical response surface and contour plots were used to locate the optimum point. The final results showed that the optimum concentration, temperature, and holding time were 10.5%, 357°C, and 37 min, respectively. Under the optimum conditions established, yield of the biocrude (41.6 ± 2.2%) was experimentally obtained using the fresh microalgae. This study showed the potential of bio-oil production of Spirulina sp. by HTL technology, but it still needs more improvement of the biocrude for utilization.

Effects of different litters on ammonia emissions from chicken manure

Different varieties of fresh lotus seeds have varying levels of amylose content. It has a direct impact on the following processing and final product quality, so the non-destructive detection of amylose content is meaningful before lotus seed production. This study proposed a non-destructive method to detect the amylose content of fresh lotus seeds. Hyperspectral images of 120 fresh lotus seeds of three different varieties were obtained, and different pretreatments were applied to the average spectra obtained from the region of interest (ROI). The calibration and prediction set were divided by the sample set joint x–y distances algorithm (SPXY). Then, the partial lease square regression (PLSR) method was established for modeling, with Savitzky–Golay pretreatment-based PLSR showing the best results. To further improve the stability of the predictive model, different methods of feature variables selection were compared. The results showed that the best PLSR model was established with the inputs of 15 feature bands selected from 472 bands by the successive projection algorithm (SPA). The correlation coefficient of the prediction set (Rp), root mean square error of the prediction set (RMSEP), and residual predictive deviation (RPD) were 0.890, 15.154 mg g^−1, and 2.193, respectively. Meanwhile, this study visualized the amylose content distribution maps from which it could estimate the content level directly. This study could provide a reference for further development of portable detection equipment for the amylose content of fresh lotus seeds.

In pig farming, multi-object tracking (MOT) algorithms are effective tools for identifying individual pigs and monitoring their health, which enhances management efficiency and intelligence. However, due to the considerable variation in breeding environments across different pig farms, existing models often struggle to perform well in unfamiliar settings. To enhance the model’s generalization in diverse tracking scenarios, we have innovatively proposed the SDGTrack method. This method improves tracking performance across various farming environments by enhancing the model’s adaptability to different domains and integrating an optimized tracking strategy, significantly increasing the generalization of group pig tracking technology across different scenarios. To comprehensively evaluate the potential of the SDGTrack method, we constructed a multi-scenario dataset that includes both public and private data, spanning ten distinct pig farming environments. We only used a portion of the daytime scenes as the training set, while the remaining daytime and nighttime scenes were used as the validation set for evaluation. The experimental results demonstrate that SDGTrack achieved a MOTA score of 80.9%, an IDSW of 24, and an IDF1 score of 85.1% across various scenarios. Compared to the original CSTrack method, SDGTrack improved the MOTA and IDF1 scores by 16.7% and 33.3%, respectively, while significantly reducing the number of ID switches by 94.6%. These findings indicate that SDGTrack offers robust tracking capabilities in previously unseen farming environments, providing a strong technical foundation for monitoring pigs in different settings.