Abstract In order to realize the rapid nondestructive detection of mildew peanut in the process of peanuts storage, hyperspectral imaging technology was proposed to detect mildew peanut. A total of 200 peanuts were selected from 5 kinds of peanuts purchased in the market for moldy treatment, and the remaining 400 peanuts were kept sterile. After completion, samples were collected with a hyperspectral instrument to obtain spectral data of the samples. According to the characteristics of the data, 10 pre-processing algorithms were used to de-noise the data, and Median Filtering (MF) had the best effect, with the recognition accuracy reaching 97.7%. GBDT, LightGBM, CatBoost and XGBoost algorithms were used to extract important feature bands in the spectral data pre-processed by MF. GBDT, LightGBM, CatBoost and XGBoost algorithms were used to model the extracted feature bands. The results showed that LightGBM is the best algorithm with a detection rate of 99.10%. Optuna algorithm was used to tune its parameters. Compared with the previous model, the running time of the optimized model was improved by about 0.25 s. The results showed that hyperspectral imaging provides an efficient and nondestructive method for detecting mildew in peanut storage.

Abstract The research constructs a novel structure by integrating two parts of online object detection pipelines into the current state-of-the-art Mask R-CNN algorithm to improve the detection performance. The DJI Mavic Air drone is used to collect low-altitude sensing images of strawberry plant canopies. The data augmentation method is employed to feed more instances into the original image dataset to boost the generalization and robustness of the detection model in the training procedure. A ResNet50 backbone combined with a feature pyramid network is presented to extract the features of strawberry plant canopies. The online hard example mining algorithm is introduced to mine hard samples to learn rich features and update model weights. Soft non-maximum suppression based on recursive application on the remaining detection boxes within the predefined overlap threshold is proposed to improve the performance of identifying the large complex overlapping area of strawberry plant canopies. The qualitative results demonstrate that the improved detection model had an AP50 of 96.9 and an AR of 78.5 on the test set, which are approximately 30% higher than the original values.

A previously reported o-nitrobenzaldehyde (ONBA) degrading bacterium Pseudomonas sp. ONBA-17 was further identified and characterized. Based on results of DNA base composition and DNA-DNA hybridization, the strain was identified as P. putida. Its degradation effect enhanced with increase of inoculum amount and no lag phase was observed. Higher removal rate was achieved under shaking conditions. All tested ONBA with different initial concentrations could be completely degraded within 5 d. In addition, degradative enzyme(s) involved was confirmed as intra-cellular distributed and constitutively expressed. Effects of different compounds on relative activity of degradative enzyme(s) within cell-free extract were also evaluated. Finally, 2-nitrobenzoic acid and 2, 3-dihydroxybenzoic acid were detected as metabolites of ONBA degradation by P. putida ONBA-17, and relevant metabolic pathway was preliminary proposed. This study might help with future research in better understanding of nitroaromatics biodegradation.