Goss Wilt is a common and serious disease during corn production. With a goal of automatic disease monitoring, this study assessed Goss's Wilt disease severity using machine (ML) and deep learning (DL) algorithms. A dataset containing 200 corn plot images was generated from an unmanned aerial vehicle (UAV) flying at five different mission heights (15, 30, 45, 60, and 75 m) above the ground level (AGL). Three different datasets including non-augmentation, segmentation and augmentation were prepared. The augmentation dataset consisting of 6200 images was prepared using geometric augmentation techniques, such as rotation, and flip. Eight different ML algorithms (i.e., Logistic Regression, Ada Boost, Gradient Boosting, Support Vector Machine, Multilayer Perceptron, Random Forest, Naive Bayes, K-Nearest Neighbors) and two different DL algorithms (i.e., GoogLeNet and ResNet18) were implanted to classify Goss Wilt severity as a binary issue (i.e., high and low). Two different types of features, including textural (contrast, dissimilarity, homogeneity, angular second moment) and color (hue, saturation, value, lightness, chromatic components: a* and b*, red, green, blue) features were extracted from individual plot image. For ML, the Random Forest yielded 0.99 precision, 0.99 recall and 0.99 F-score in augmented dataset and outperformed all other classifiers. For DL, Resnet18 achieved slightly better results: 0.81 precision, 0.78 recall and 0.79 F-score than GoogleNet, which has 0.75 precision, 0.70 recall, and 0.73 F-score. The ML model (Random Forest) performed satisfactorily by resulting in higher precision, recall and F-score in augmented dataset. However, ML models underperformed on segmentation dataset. Therefore, Random Forest coupled with UAV imagery is a potential valuable tool for automatic assessment of Goss Wilt disease.
Agriculture products are essential to the globe since they meet all of the requirements for human sustenance; thus, constantly developing new methods and equipment to increase output and stability is crucial. The apple is one of the most significant fruits grown worldwide. It offers nutritional and health advantages; therefore, it is essential to increase output and ensure quality by creating intelligent tools and equipment. This study analyzes the growth of intelligent, automated apple fruit equipment in five stages: picking, pruning, thinning, pollinating, and bagging. First, summarizing robots, applications, resources, and findings; next, identifying noteworthy advancements and tactics; then, highlighting the significant difficulties; and lastly, outlining potential prospects and our outlook for the future. They all contribute to providing services and maintaining the growth of research communities for increased apple fruit productivity and quality.
Twenty-eight bryophyte taxa in 17 genera of 10 families from Xinlu Carlin gold field were investigated.Nine species belong to Pottiaceae and 8 species belong to Bryaceae.Among them,Pottiaceae and Bryaceae are the dominate families.By analyzing β diversity of four sites in this area,we found that β diversity difference between barren rock field-waste residue field and relatively polluted area-clear area is the largest,bryophyte community structure is the biggest difference,and similarity is the lowest.A particular regularity was proved by monitoring 6 kinds of heavy metals Pb,Zn,Cu,Cd,Hg,As of bryophytes and their substrates from different areas in Xinlu Carlin gold deposit,that is waste residue fieldbarren rock fieldrelatively polluted areaclear area.It might be closely related to the mining and smelting.Obviously,further researches on bryophytes can be used to monitor the pollution of heavy metals in gold deposits.
Lychee pericarp browning index increased while the content of anthocyanins decreased with storage time, and there was a good correlation between pericarp anthocyanin content and browning index when the pH-differential method was used as the analytic method. However, there was no obvious change in the content of anthocyanins while the fruit browned when the acidific methanol method was used. To confirm the correlationship between pericarp browning and anthocyanins content, these two methods was compared. Besides anthocyanins, acid methanol also extracted the brown compounds, which were insoluble in water and interfered with the determination. According to the solubility of anthocyanins in water, φ =1% HCl was used as the extract solution in the pH-differential method, which would not extract the brown compounds. Additionally pH differential method could eliminate the interference of other compounds with the determination.
The geologic abnormal bodies such as faults,subsided columns and fractured zones which were encountered in the excavation process of roadways will directly affect its excavation efficiency,and will also threaten the safe production of the mine.The use of seismic waves for roadway advance detection can accurately predict the position of the fault structure and its abnormal interface in front of the heading face.This paper described the application results of advance detection with seismic reflection wave method based on the discussion of data acquisition,data processing and interpretation methods and according to the real detection examples of the mine roadway.
Abstract. Automatic bin filling is needed for apple harvest and in-field sorting. A commercially viable bin filler for in-field use should be simple, compact, low in cost, and be able to distribute apples evenly in the bin without causing bruising damage. An innovative bin filling technology was developed for incorporation with the new apple harvest and in-field sorting machine recently developed by our group. Field tests of the first version of the bin filler in the 2016 harvest season showed relatively high bruising rates and uneven fruit distributions. Subsequently, a second version of the bin filler was developed with several major improvements. A new pair of foam rollers for better control of apples exiting the sorting system and avoiding fruit collisions during free falling was added below the sorter. An improved pinwheel with nine longer soft pads, instead of four short pads as in the original version, was installed for better fruit distribution. Foam guides, attached to the long pads, reduced the rolling speed of fruit from the pads into the bin. Field tests conducted in the 2017 harvest season showed that the second, improved version of the bin filler achieved superior performance in reducing bruise damage, with 99% of ‘Gala’ apples and 98% ‘Blondee’ of apples graded Extra Fancy. Furthermore, a depth imaging method, using a Kinect-v2 camera, was proposed to quantitatively compare the performance of the two bin fillers for distribution of fruit in the bin under uniform and non-uniform feeding conditions. Analysis of the fruit height data showed that the apple distributions were not significantly affected by feeding method for both bin fillers. Overall, the second version of the bin filler resulted in better distributions of apples in the bin, compared to the first version, and uneven distributions mainly occurred in the corners of the bin, which could not be reached by the bin filler’s pinwheel. The improved bin filler meets the requirements for apple harvest and in-field sorting, and it has potential for use with other harvest platforms. Keywords: 3D imaging, Apple, Bin filling, Bruising, Harvest, Sorting.
The SHRIMP U Pb zircon data of a diabase from the Aoyougou ophiolite in the west sector of north Qilian Mountains are reported in the paper. It shows that the Aoyougou ophiolite was formed in the early middle Proterozoic (about 1777Ma), and proves that it is the earliest ophiolite that has been reported in China. In addition, the dating implies that two geological events occurred about 1466 Ma, and 507 Ma, respectively. The former one probably represents the least closure age of the ocean basin of the early middle Proterozoic whereas the latter one represents the Caledonian regional metamorphism. Moreover, Late Archean crystallized basement (about 2561 Ma) may present in the area although it did not outcrop.
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