Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases.

Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets of weeds in the field has limited the application of deep learning techniques in weed management. In this paper, it presented a dataset of weeds in fields, Weed25, which contained 14,035 images of 25 different weed species. Both monocot and dicot weed image resources were included in this dataset. Meanwhile, weed images at different growth stages were also recorded. Several common deep learning detection models—YOLOv3, YOLOv5, and Faster R-CNN—were applied for weed identification model training using this dataset. The results showed that the average accuracy of detection under the same training parameters were 91.8%, 92.4%, and 92.15% respectively. It presented that Weed25 could be a potential effective training resource for further development of in-field real-time weed identification models. The dataset is available at https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw; the password is rn5h.

Herbicide deposition rate can be affected by the leaf surface features of weeds and have a significant impact on the overall efficacy. In this paper, an orthogonal experiment was conducted to investigate the differences of droplet evaporation and spreading characteristics corresponding to weed leaf surface with hairy, waxy and rough (ridged) structures. Three weed species—Descurainia sophia, Lepidium lotifolium, and Lolium temulentum—were included in the study, representing these three leaf structures respectively. Glyphosate sprays with organic silicone surfactant in different concentrations were composed for the test. Single droplets with two diameters of 0.05 μL and 0.1 μL were deposited on the leaves to evaluate the evaporation and spreading characteristics. A digital camera was used and the evaporation duration and the maximum droplet coverage images could be captured and extracted from the recorded videos. The Image Processing Toolbox in Matlab was applied to segment the images for droplet and leaf background and the binary images’ pixel numbers were counted for coverage area calculation. The results revealed that the evaporation duration was reduced with the increase of the organic silicone concentration, while the spread area was expanded. The droplet spread more widely and evaporated faster on D. sophia leaves than on the leaves with L. lotifolium and L. temulentum surfaces. The spreading area and evaporation duration varied much faster on L. lotifolium leaves than on the leaves of other weed species. The droplet sizes affected spreading more significantly on L. temulentum leaf surface, as the spreading procedure of small size droplets was restrained by the groove structure. The results of this study would benefit the consideration of the farmers when selecting the proper nozzle code and the determining of the surfactant mixture in order to optimize the use of herbicides like glyphosate.

Herbicides may damage soybean in conventional production systems. Chlorophyll fluorescence imaging technology has been applied to identify herbicide stress in weed species a few days after application. In this study, greenhouse experiments followed by field experiments at five sites were conducted to investigate if the chlorophyll fluorescence imaging is capable of identifying herbicide stress in soybean shortly after application. Measurements were carried out from emergence until the three-to-four-leaf stage of the soybean plants. Results showed that maximal photosystem II (PS II) quantum yield and shoot dry biomass was significantly reduced in soybean by herbicides compared to the untreated control plants. The stress of PS II inhibiting herbicides occurred on the cotyledons of soybean and plants recovered after one week. The stress induced by DOXP synthase-, microtubule assembly-, or cell division-inhibitors was measured from the two-leaf stage until four-leaf stage of soybean. We could demonstrate that the chlorophyll fluorescence imaging technology is capable for detecting herbicide stress in soybean. The system can be applied under both greenhouse and field conditions. This helps farmers to select weed control strategies with less phytotoxicity in soybean and avoid yield losses due to herbicide stress.

Introduction: Precise identification of crop insects is a crucial aspect of intelligent plant protection. Recently, with the development of deep learning methods, the efficiency of insect recognition has been significantly improved. However, the recognition rate of existing models for small insect targets is still insufficient for insect early warning or precise variable pesticide application. Small insects occupy less pixel information on the image, making it more difficult for the model to extract feature information.Methods: To improve the identification accuracy of small insect targets, in this paper, we proposed S-ResNet, a model improved from the ResNet, by varying its convolution kernel. The branch of the residual structure was added and the Feature Multiplexing Module (FMM) was illustrated. Therefore, the feature expression capacity of the model was improved using feature information of different scales. Meanwhile, the Adjacent Elimination Module (AEM) was furtherly employed to eliminate the useless information in the extracted features of the model.Results: The training and validation results showed that the improved residual structure improved the feature extraction ability of small insect targets compared to the original model. With compare of 18, 30, or 50 layers, the S-ResNet enhanced the identification accuracy of small insect targets by 7% than that on the ResNet model with same layer depth.

Herbicide resistant weeds need to be identified early so that yield loss can be avoided by applying proper field management strategies. A novel chlorophyll-fluorescence-imaging sensor has been developed to conduct real-time herbicide effect evaluation. In this research, greenhouse and field experiments were conducted to calibrate the capability of the sensor in monitoring herbicide effects on different biotypes of two grass weeds (Alopecurus myosuroides, Apera spica-venti) in southwestern Germany. Herbicides with different modes of action were applied for the effect monitoring. Chlorophyll fluorescence yield of the plants was measured 3–15 days after treatment (DAT) using the new fluorescence sensor. Visual assessment of the weeds was carried out on 21 DAT. The results showed that the maximal PS II quantum yield (F_v/F_m) of herbicide sensitive weeds was significantly lower than the values of resistant populations in 5 DAT. The new technology was capable of quickly identifying the herbicide’s effect on plants. It can be used to optimize management strategies to control herbicide resistant weeds.