The chlorophyll, pheophytin, and their proportions are critical factors to evaluate the sensory quality of green tea. This research aims to establish an effective method to determine the quantification of chlorophyll and pheophytin in green tea, based on Fourier transform infrared (FT–IR) spectroscopy. First, five brands of tea were collected for spectral acquisition, and the chlorophyll and pheophytin were measured using the reference method. Then, a relation between these two pigments and FT–IR spectroscopy were developed based on chemometrics. Additionally, the characteristic IR wavenumbers of these pigments were extracted and proved to be effective for a quantitative determination. Successively, non-linear models were also built based on these characteristic wavenumbers, obtaining coefficients of determination of 0.87, 0.80, 0.85 and 0.89; and relative predictive deviations of 2.77, 2.62, 2.26 and 3.07 for the four pigments, respectively. These results demonstrate the feasibility of FT–IR spectroscopy for the determination of chlorophyll and pheophytin.

The wettability of plant leaves directly reflects leaf hydrophilicity, which is the key factor that influences the adhesion of liquid pesticide as well as affects plant protection products (PPP) efficacy. Generally, the wettability of leaf surface is quantified by the contact angle and surface free energy (SFE), which are mainly dependent on leaf surface properties, liquid properties and other spraying parameters. Therefore, the aim of this paper was to investigate the SFE of rice and rape leaves with the variation of leaf status, leaf surface, and probe liquid as well as the influence of droplet falling height, solid surface, and PPP concentration on the wettability. The results showed that: (1) the dispersive components of SFE of rice and rape account for a large proportion which are closely related to their hydrophobicity—the abaxial of rape new leaf and the adaxial of rape old leaf are easier to wet comparing with rice and rape leaves in other statuses; (2) the increase of droplet falling height had a significant effect on improving the wettability between wax surface and adjuvant solution, while it had little improving effect on the wettability between wax surface and water; (3) the wettability of different solid surface varied greatly, and the order of wettability from good to bad is water-sensitive paper (WSP), wax, rape leaf, and rice leaf; (4) the effect of PPP concentration on the leaf surface wettability is significant, the contact angle decreased with the increase of PPP concentration, and the wettability of microemulsion is better than that of suspending agent and wettable powder. In conclusion, the SFE and wettability of crop leaf surface determine the suitable type of PPP, studying the influence of multiple factors on leaf surface wettability can provide a reliable reference for providing scientific guidance as well as improving the effective utilization of PPP.

Using intelligent agricultural machines in paddy fields has received great attention. An obstacle avoidance system is required with the development of agricultural machines. In order to make the machines more intelligent, detecting and tracking obstacles, especially the moving obstacles in paddy fields, is the basis of obstacle avoidance. To achieve this goal, a red, green and blue (RGB) camera and a computer were used to build a machine vision system, mounted on a transplanter. A method that combined the improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracking (deep SORT) was used to detect and track typical moving obstacles, and figure out the center point positions of the obstacles in paddy fields. The improved Yolov3 has 23 residual blocks and upsamples only once, and has new loss calculation functions. Results showed that the improved Yolov3 obtained mean intersection over union (mIoU) score of 0.779 and was 27.3% faster in processing speed than standard Yolov3 on a self-created test dataset of moving obstacles (human and water buffalo) in paddy fields. An acceptable performance for detecting and tracking could be obtained in a real paddy field test with an average processing speed of 5–7 frames per second (FPS), which satisfies actual work demands. In future research, the proposed system could support the intelligent agriculture machines more flexible in autonomous navigation.

The object detection method based on deep learning convolutional neural network (CNN) significantly improves the detection performance of wheat head on wheat images obtained from the near ground. Nevertheless, for wheat head images of different stages, high density, and overlaps captured by the aerial-scale unmanned aerial vehicle (UAV), the existing deep learning-based object detection methods often have poor detection effects. Since the receptive field of CNN is usually small, it is not conducive to capture global features. The visual Transformer can capture the global information of an image; hence we introduce Transformer to improve the detection effect and reduce the computation of the network. Three object detection networks based on Transformer are designed and developed, including the two-stage method FR-Transformer and the one-stage methods R-Transformer and Y-Transformer. Compared with various other prevalent object detection CNN methods, our FR-Transformer method outperforms them by 88.3% for AP₅₀ and 38.5% for AP₇₅. The experiments represent that the FR-Transformer method can gratify requirements of rapid and precise detection of wheat heads by the UAV in the field to a certain extent. These more relevant and direct information provide a reliable reference for further estimation of wheat yield.

Background: Pumpkin seeds are major oil crops with high nutritional value and high oil content. The collection and identification of different pumpkin germplasm resources play a significant role in the realization of precision breeding and variety improvement. In this research, we collected 75 species of pumpkin from the Zhejiang Province of China. 35,927 near-infrared hyperspectral images of 75 types of pumpkin seeds were used as the research object. Results: To realize the rapid classification of pumpkin seed varieties, position attention embedded three-dimensional convolutional neural network (PA-3DCNN) was designed based on hyperspectral image technology. The experimental results showed that PA-3DCNN had the best classification effect than other classical machine learning technology. The classification accuracy of 99.14% and 95.20% were severally reached on the training and test sets. We also demonstrated that the PA-3DCNN model performed well in next year’s classification with fine-tuning and met with 94.8% accuracy. Conclusions: The model performance improved by introducing double convolution and pooling structure and position attention module. Meanwhile, the generalization performance of the model was verified, which can be adopted for the classification of pumpkin seeds in multiple years. This study provided a new strategy and a feasible technical approach for identifying germplasm resources of pumpkin seeds.

The non-judicious use of pesticides in agro-food poses a severe threat to food safety and human health. As an emerging chromatographic fingerprint provider, surface-enhanced Raman spectroscopy analysis (SERS) sheds bright light on sensitive and nondestructive detection of pesticide residues. This research proposed a novel strategy to detect three-pesticide residues (thiabendazole, carbendazim, and chlorpyrifos) on tomato peel based on the flexible and sticky SERS substrate. After selecting the best commercial adhesive tape (3M9080), the SERS substrate was constructed by optimizing the parameters in the preparation process of AuNPs. Therefore, a new simple tape-wrapped SERS way for pesticide residue analysis was established with a simple procedure of absorption, separation, and drop addition. Based on chemometrics method, the limit of semiquantitative detection was 20, 36, and 80ng/cm² for thiabendazole, carbendazim, and chlorpyrifos, respectively, on tomato surface, which indicated that the proposed method could meet the requirement of actual application with a large prospect in agro-food safety detection.