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Water stress has been one of the most serious threat to the crop growth, development and yield quality in agricultural fields. Timely and accurate diagnosis of crop water stress can greatly contribute to the precision irrigation for the crop resilience and yield. In this study, the research object was taken from the summer maize in the typical dryland agricultural area of northwest China. A six-channel multispectral sensor was mounted on a drone to obtain the remote sensing image data of summer maize at the nodulatione and staminate pulling stage in 2022. At the same time, the stomatal conductance and phenotypic parameters of summer maize were also collected. The background was removed by supervised classification. The canopy vegetation index and image texture were obtained using the gray-scale covariance matrix. The sensitive vegetation index, image texture and phenotypic parameters and their combinations were screened out by the Bayesian information criterion and full subset filtering. The summer maize stomatal conductance estimation model was constructed to combine the three types of machine learnings: the extreme learning machine, the random forest, and the back-propagation neural network. The optimal model was mapped to estimate the stomatal conductance. The Pearson correlation coefficient of vegetation index and stomatal conductance were significantly positively correlated, whereas, the canopy reflectance of summer maize was weakly negatively correlated. Different types of image textures at different wavelengths were correlated with the stomatal conductance, and the highest correlation was found in the 550 nm band. The Pearson correlation coefficients between morphological structure phenotypes (plant height, stem thickness and leaf area) and stomatal conductance of summer maize were 0.72, 0.58 and 0.69, respectively, where the three types of phenotypic parameters data were correlated well with stomatal conductance. Vegetation indices with spectral reflectance data were used to assess the overall health and moisture status of the vegetation. Image texture was used to capture the spatial distribution, texture and structural features of crops. Crop phenotypic parameters were then used to reflect the physiological and morphological responses of the crop in a three-dimensional manner, providing visual information about the growth and moisture of the vegetation. The decision coefficients of the crop water stress diagnostic models that constructed from the three information sources increased from 0.728 and 0.750 to 0.841, respectively, compared with the single or two combinations, indicating the great potential to stomatal conductance prediction. The optimal combination of indicators was screened by Bayesian information criterion and full subset screening: DWSI, NDVI, MEA, ENT, plant height and leaf area. The back-propagation neural network model with the three complementary information sources was the optimal model for the water stress diagnosis of summer maize (coefficient of determination of 0.841, root mean square error of 0.043 mol/(m2 ·s), and mean absolute error of 0.034 mol/(m² ·s)). The underestimation of stomatal conductance was significantly improved, compared with the rest models. The inverse map with the optimal model was widely applied to easily and accurately diagnose the crop water stress for the purpose of irrigation strategies and resource allocation. The finding can provide a feasible and accurate diagnosis of water stress in summer maize. [ABSTRACT FROM AUTHOR]

Currently, about 60% of the world’s fish come from China. Our nation is the biggest aquaculture country in the world. However, China's traditional aquaculture farming methods still plays an important role. This will lead to a waste of hunman resources and land resources. In recent years, more than 90% of the fish farming workers prefer to apply RAS (recirulating aquaculture systems). Because RAS (recirulating aquaculture systems) not only create a good rearing conditions and feed utilization for the good growth of the shoal, but also provide a necessary prerequisite for the sustainable development of the aquaculture. Although RAS has a total of more convenience, nevertheless, it still has a lot of problems: the over-feeding events continues to affect the welfare of shoal, the operation of systems also influence the growth and survival of shoal, in addition, the water quality of the system increasing the risk of diseases and leading to extra-load on the mechanical filters, bio-filters and oxygenation equipments. Therefore, the most direct and effective way to solve this problem is to determine the amount of feeding activities according to the appetite of the shoal. In actual operation, manual observation is an effective method. But, on the one hand, this way requires fish-farming workers to increase labor and time costs. On the other hand, the most difficult problem is how to measure feeding activity. This is becauset he reslut of visual observation for the shoal appetite is often hampered by high shoal density and some other unfavorable factors .In recent years, several methods have been developed to enable tracking of several fish in the same tank, but these methods are invasive, potentially disturbing, or need restricting conditions. In order to solve this problem, a novel method, with the help of the original method based on image texture of the Cyprinus carpio specularis, is proposed to evaluate the feeding activity of the shoal. With this method, the feeding activity of Cyprinus carpio specularis is recorded by a camera mounted above the rearing tank. Then, the video is decomposed into a sequence frame image, remove the background interference, extract the foreground object. Next, the texture of shoal is extracted by using gray level cooccurrence matrix. By this way, it can avoid the interference of surface vibration, spray and other adverse factors. At last, we find out the similarity and difference of texture features. To sum up the above arguments, this method can be used for quantifing analysis of the feeding activity of the shoal. To evaluate the reliability of shoal image texture, the other way is put forward to quantify the feeding activity of shoal. Before this research, the diffusion and aggregation indices used by some researchers describe the feeding activity of shoal. The index is calculated by summing the individual area of each group. A group is a single black area created by the outlines of overlapping fish. Using this method, it is easy to observe that when the shoal is spread, the index would be high, otherwise, the index would be low. The correlation coefficient of the above methods is 0.8942. Hence, the feeding activity of fish can be quantified by the image texture of the shoal. This method provides a sensitive, non-invasive, simple and widely applicable tool to quantify the behavior changes associated with the various challenges in aquaculture conditions. [ABSTRACT FROM AUTHOR]

Here, a computer vision on-line detection system was applied to Shiitake mushrooms (Lentinus edodes) during the hot air drying with the process control of Relative Humidity (RH). Four RH controls were used, including continuous fanning, constant target RH throughout the whole process, reducing the RH at different stages, and rapidly reducing the RH at the later stage. An image processing using region segmentation was selected to on-line measure the Ratio of Wrinkled Surface Area (RWSA), in order to separate the wrinkled areas from the non-wrinkled areas on the surface of shiitake mushrooms. The roundness in the image was used to represent the shape of the edge of shiitake mushrooms. The Cell Aspect Ratio (CAR) was used to quantify the degree of deformation in a cell of shiitake mushrooms, which was calculated by the pixel length of cells along the radial and vertical direction in the microscopic image. The appearance features (contrast and energy) of shiitake mushrooms were obtained by the Gray Level Co-occurrence Matrix (GLCM). A measurement was made for the changes of shrinkage, roundness, RWSA, and visible texture (contrast and energy) characteristics during the drying process of shiitake mushrooms, and thereby to establish the relationship among appearance qualities, Rehydration Ratio (RR), and microstructure of shiitake mushrooms. The results showed that the RH had significant effects (P < 0.05) on the appearance qualities (shrinkage, roundness, RWSA, contrast, and energy), the microstructure, and rehydration ratio of shiitake mushrooms. Compared with the continuous fanning, higher RH maintained in the drying room at the initial stage of drying, corresponding to higher internal temperature of shiitake mushrooms, while longer drying time. The higher the initial RH or the longer the RH holding time was, the lower the drying rate and surface hardening degree were. Therefore, the shiitake mushrooms were more sensitive to shrinkage and deformation. The roundness changes of shiitake mushrooms corresponded to the change of shrinkages, where the higher the shiitake mushrooms shrinkage was, the lower the roundness was, indicating the more irregular shape of shiitake mushrooms. The slow rate of surface hardening led to more wrinkles on the surface of shiitake mushrooms, and lower color contrast distribution in the neighborhood pixels on the images, indicating that the color of non-wrinkled area was close to that of wrinkled area, and the whole surface color of shiitake mushroom was darker. Meanwhile, the cell aspect ratio of shiitake mushrooms cells was larger, indicating that the deformation of cells was more serious. At the same time, the small cell pore resulted in very low rehydration ratio of shiitake mushrooms. Compared with the whole process groups, the drying time, and shrinkage were significantly (P < 0.05) reduced, the roundness improved significantly (P < 0.05), the RWSAs reduced (P > 0.05), and the surface color of shiitake mushrooms lighter (P < 0.05) in the optimized groups by turning on the dehumidification fan, when the moisture evaporation was not enough to maintain the set RH range in the drying room. The reason can be the increased differential partial pressure between the air and the surface of shiitake mushrooms, resulting in an accelerated drying rate, while rapidly hardened and shaped surface of shiitake mushrooms. However, the deformation degree of shiitake mushroom cells was not significantly (P > 0.05) reduced, and there was no increase in the erehydration ratio. A positive correlation was observed between the time, when the RWSA reached the maximum and the rehydration ratio of shiitake mushrooms (r = 0.88). High relative humidity had a negative effect on the flavor components of shiitake mushrooms. The best drying was continuous fanning, according to the drying time, appearance quality, sensory quality, and composition of dried shiitake mushrooms. [ABSTRACT FROM AUTHOR]