Detection of Ecballium elaterium in hedgerow olive orchards using a low-cost uncrewed aerial vehicle and open-source algorithms

BACKGROUND

Ecballium elaterium (common name: squirting cucumber) is an emerging weed problem in hedgerow or superintensive olive groves under no tillage. It colonizes the inter-row area infesting the natural or sown cover crops, and is considered a hard-to-control weed. Research in other woody crops has shown E. elaterium has a patchy distribution, which makes this weed susceptible to design a site-specific control strategy only addressed to E. elaterium patches. Therefore, the aim of this work was to develop a methodology based on the analysis of imagery acquired with an uncrewed aerial vehicle (UAV) to detect and map E. elaterium infestations in hedgerow olive orchards.

RESULTS

The study was conducted in two superintensive olive orchards, and the images were taken using a UAV equipped with an RGB sensor. Flights were conducted on two dates: in May, when there were various weeds infesting the orchard, and in September, when E. elaterium was the only infesting weed. UAV-orthomosaics in the first scenario were classified using random forest models, and the orthomosaics from September with E. elaterium as the only weed, were analyzed using an unsupervised algorithm. In both cases, the overall accuracies were over 0.85, and the producer's accuracies for E. elaterium ranged between 0.74 and 1.00.

CONCLUSION

These results allow the design of a site-specific and efficient herbicide control protocol which would represent a step forward in sustainable weed management. The development of these algorithms in free and open-source software fosters their application in small and medium farms. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

基于森林资源节约的出版规范改进研究

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Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

In this study, the prediction of pine mistletoe distribution in Scots pine ecosystems was explored using remote sensing variables to compare the multilayer perceptron (MLP) artificial neural network (ANN) and logistic regression (LR) model performances. For this purpose, 109 sample plots were distinguished in pure Scots pine forests (natural) in the Eastern Black Sea Region of Turkey. Distinguishing mistletoe-infected stands (69) and uninfected stands (40) was performed with field observations. The variables acquired from Landsat 8 (Level 1) images were used as independent variables for independent-sample t-test, MLP ANN and LR models. Remote sensing variables indicated that mistletoe-infected stands were in drier areas with a lower vegetation-leaf area index. Based on the performance results of both models, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy of the MLP ANN model were superior to those of the LR model. The prediction percentages (SEN, SPE, PPV and NPV) of mistletoe-infected stands were better than the prediction percentages of uninfected stands. The prediction accuracies of LR and MLP ANN models were 74.3% and 89.6%, respectively. However, all remote sensing variables were included in the prediction equation of the MLP ANN model, while the thermal infrared 1 (TIRS1) variable was included in the LR model. In the MLP ANN model, the TIRS1 variable also had the highest normalized importance (100%). The area under the curve (AUC) value for identifying the mistletoe-infected stands of Scots pine forests used by the MLP ANN model (0.892 ± 0.034) was higher than in the LR model (0.838 ± 0.039), explaining the more accurate predictions obtained from the MLP ANN model. The MLP ANN model showed much better performance than the LR model. The results of this study are expected to make important contributions to the identification of potential mistletoe-infected areas.     

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绥化市秦家镇农田防护林网络架构及生态农业效益分析

对松嫩平原上一典型土地开发整理项目区深入调查研究基础上,在人均土地面积较少的农村,构建出由农田防护林、水利排灌、道路、农田4项网络构成的高效农田防护林复合经营与多样型林带乔、灌立体混交经营模式,实现了林带由单一结构向多层次的立体结构转移,架构了生态立体农业体系,旨为重新调整土地利用结构,实现山、水、田、林、路的全面治理提供依据。     

嫩江沙地杨树用材林生产力的研究

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国有林场森林资源信息管理系统研究及应用

以登封国有林场为试验对象,开发了国有林场森林资源信息管理系统,并得到应用。基本上实现了登封国有林场森林资源管理的信息化,为森林资源的科学管理和决策,以及可持续发展提供了技术支持。     

林木耐旱机制研究进展

水分胁迫是制约林木生长发育的重要环境因子,在深入研究林木耐旱机制的基础上培育抗旱林木是摆在当代林学家面前艰巨的任务。本文从林木对干旱胁迫信号传导,林木在干旱胁迫下生理生化反应以及启动的防卫机制几个方面进行了评述和探讨。     

经济林种子生理研究进展

经济林种子是育苗、造林的物质基础,种子品质的好坏直接关系到苗木生产及造林的成效。本文就经济林种子的休眠与萌发、种子的老化及劣变、种子活力等种子生理工作研究现状进行了较为系统的总结,并提出了今后经济林种子生理的研究方向和发展趋势。     

三峡库区林草间作及肉羊高效养殖模式探讨

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