A high-precision jujube disease spot detection based on SSD during the sorting process.

The development of automated grading equipment requires achieving high throughput and precise detection of disease spots on jujubes. However, the current algorithms are inadequate in accomplishing these objectives due to their high density, varying sizes and shapes, and limited location information regarding disease spots on jujubes. This paper proposes a method called JujubeSSD, to boost the precision of identifying disease spots in jujubes based on a single shot multi-box detector (SSD) network. In this study, a diverse dataset comprising disease spots of varied sizes and shapes, varying densities, and multiple location details on jujubes was created through artificial collection and data augmentation. The parameter information obtained from transfer learning into the backbone feature extraction network of the SSD model, which reduced the time of spot detection to 0.14 s. To enhance the learning of target detail features and improve the recognition of weak information, the traditional convolution layer was replaced with deformable convolutional networks (DCNs). Furthermore, to address the challenge of varying sizes and shapes of disease spot regions on jujubes, the path aggregation feature pyramid network (PAFPN) and balanced feature pyramid (BFP) were integrated into the SSD network. Experimental results demonstrate that the mean average precision at the IoU (intersection over union) threshold of 0.5 (mAP@0.5) of JujubeSSD reached 97.1%, representing an improvement of approximately 6.35% compared to the original algorithm. When compared to existing algorithms, such as YOLOv5 and Faster R-CNN, the improvements in mAP@0.5 were 16.84% and 8.61%, respectively. Therefore, the proposed method for detecting jujube disease spot achieves superior performance in jujube surface disease detection and meets the requirements for practical application in agricultural production.

Direct evidence of lead contamination in wheat tissues from atmospheric deposition based on atmospheric deposition exposure contrast tests.

A field experiment was conducted to assess the atmospheric deposition effects on lead (Pb) contamination in wheat by two contrasting treatments: wheat exposed or not to atmospheric deposition. Plants were housed in a shed during wheat greening for the non-exposed treatment. The Pb contents of wheat during different growth stages, of soil and of atmospheric deposits were analysed and combined with Pb stable isotope data to quantify the contribution of atmospheric deposition and soil to Pb in wheat tissue. The Pb content in atmospheric deposits was significantly higher than those in soil and wheat tissue, and the Pb content in wheat tissue exposed to atmospheric deposition was significantly higher than the Pb content in non-exposed tissue (p < 0.05). The 206Pb/207Pb of soil was significantly higher than the 206Pb/207Pb of atmospheric deposits (p < 0.05), and soil and atmospheric deposition were the two sources of Pb in wheat tissue. Atmospheric deposition was the main source of wheat tissue Pb in the exposed treatment, and most of the wheat tissue Pb, except for that in the stem, also came from atmospheric deposition in the maturing stage. The proportion of Pb from atmospheric deposition in roots, stems and leaves evidently decreased after the shed was erected, and the contribution of Pb from atmospheric deposition to wheat tissue was significantly higher in the exposed treatment than in the non-exposed treatment (p < 0.05). This contrast test directly confirmed that atmospheric deposition was the main source of Pb in the wheat tissues. Therefore, taking measures to reduce the absorption of Pb by wheat from atmospheric deposition can effectively ensure food safety.