Climate change amplifies plant invasion hotspots in Nepal

Aim Climate change has increased the risk of biological invasions, particularly by increasing the climatically suitable regions for invasive alien species. The distribution of many native and invasive species has been predicted to change under future climate. We performed species distribution modelling of invasive alien plants (IAPs) to identify hotspots under current and future climate scenarios in Nepal, a country ranked among the most vulnerable countries to biological invasions and climate change in the world. Location Nepal. Methods We predicted climatically suitable niches of 24 out of the total 26 reported IAPs in Nepal under current and future climate (2050 for RCP 6.0) using an ensemble of species distribution models. We also conducted hotspot analysis to highlight the geographic hotspots for IAPs in different climatic zones, land cover, ecoregions, physiography and federal states. Results Under future climate, climatically suitable regions for 75% of IAPs will expand in contrast to a contraction of the climatically suitable regions for the remaining 25% of the IAPs. A high proportion of the modelled suitable niches of IAPs occurred on agricultural lands followed by forests. In aggregation, both extent and intensity (invasion hotspots) of the climatically suitable regions for IAPs will increase in Nepal under future climate scenarios. The invasion hotspots will expand towards the high‐elevation mountainous regions. In these regions, land use is rapidly transforming due to the development of infrastructure and expansion of tourism and trade. Main conclusions Negative impacts on livelihood, biodiversity and ecosystem services, as well as economic loss caused by IAPs in the future, may be amplified if preventive and control measures are not immediately initiated. Therefore, the management of IAPs in Nepal should account for the vulnerability of climate change‐induced biological invasions into new areas, primarily in the mountains

A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management

In this work we demonstrate a rapidly deployable weed classification system that uses visual data to enable autonomous precision weeding without making prior assumptions about which weed species are present in a given field. Previous work in this area relies on having prior knowledge of the weed species present in the field. This assumption cannot always hold true for every field, and thus limits the use of weed classification systems based on this assumption. In this work, we obviate this assumption and introduce a rapidly deployable approach able to operate on any field without any weed species assumptions prior to deployment. We present a three stage pipeline for the implementation of our weed classification system consisting of initial field surveillance, offline processing and selective labelling, and automated precision weeding. The key characteristic of our approach is the combination of plant clustering and selective labelling which is what enables our system to operate without prior weed species knowledge. Testing using field data we are able to label 12.3 times fewer images than traditional full labelling whilst reducing classification accuracy by only 14%.Comment: 36 pages, 14 figures, published Computers and Electronics in Agriculture Vol. 14

EFRC Bulletin 81 December 2005

EFRC's regular Bulletin with updates from the Organic Advisory Servic

Combining physical and cultural weed control with biological methods – prospects for integrated non-chemical weed management strategies

The paper deals with the possibilities of combining physical weed control with biological weed control

Recent results in the development of band steaming for intra-row weed control

The recent achievements with developing band-steaming techniques for intra-row weed control in vegetables are presente

Sowing time, false seedbed, row distance and mechanical weed control in organic winter wheat.

In organic farming, mechanical weed control in winter wheat is often difficult to carry out in the fall, and may damage the crop, and weed harrowing in the spring is not effective against erect, tap-rooted weeds such as Tripleurospermum inodorum, Papaver rhoeas, Brassica napus and others which have been established in the autumn. Some experiments concerning sowing strategy and intensity of mechanical weed control, which included row distance, were conducted. The results underline the importance of choosing weed control strategy, including preventive measures, according to the weed flora in the field. In the experiment with low weed pressure and without erect weeds, there was very little effect of sowing strategy and row distance. In such a case, the winter wheat might as well be sown early, in order to avoid possible yield loss by later sowing, and at normal row distance to enhance the competitiveness of the crop. In the experiments with high weed pressure and erect weeds, the weed control was better with late sowing and large row distance (high intensity control), even though this was not always reflected in the yield. However, the trade-off for lower input to the soil seed bank in organic systems should be enough to balance off the risk of smaller yield

Plant Localization and Discrimination using 2D+3D Computer Vision for Robotic Intra-row Weed Control

Weed management is vitally important in crop production systems. However, conventional herbicide based weed control can lead to negative environmental impacts. Manual weed control is laborious and impractical for large scale production. Robotic weed control offers a possibility of controlling weeds precisely, particularly for weeds growing near or within crop rows. A computer vision system was developed based on Kinect V2 sensor, using the fusion of two-dimensional textural data and three-dimensional spatial data to recognize and localized crop plants different growth stages. Images were acquired of different plant species such as broccoli, lettuce and corn at different growth stages. A database system was developed to organize these images. Several feature extraction algorithms were developed which addressed the problems of canopy occlusion and damaged leaves. With our proposed algorithms, different features were extracted and used to train plant and background classifiers. Finally, the efficiency and accuracy of the proposed classification methods were demonstrated and validated by experiments

Development of an optical sensor for real-time weed detection using laser based spectroscopy

The management of weeds in agriculture is a time consuming and expensive activity, including in Australia where the predominant strategy is blanket spraying of herbicides. This approach wastes herbicide by applying it in areas where there are no weeds. Discrimination of different plant species can be performed based on the spectral reflectance of the leaves. This thesis describes the development of a sensor for automatic spot spraying of weeds within crop rows. The sensor records the relative intensity of reflected light in three narrow wavebands using lasers as an illumination source. A prototype weed sensor which had been previously developed was evaluated and redesigned to improve its plant discrimination performance. A line scan image sensor replacement was chosen which reduced the noise in the recorded spectral reflectance properties. The switching speed of the laser sources was increased by replacing the laser drivers. The optical properties of the light source were improved to provide a more uniform illumination across the viewing area of the sensor. A new opto-mechanical system was designed and constructed with the required robustness to operate the weed sensor in outdoor conditions. Independent operation of the sensor was made possible by the development of hardware and software for an embedded controller which operated the opto-electronic components and performed plant discrimination. The first revised prototype was capable of detecting plants at a speed of 10 km/h in outdoor conditions with the sensor attached to a quad bike. However, it was not capable of discriminating different plants. The final prototype included a line scan sensor with increased dynamic range and pixel resolution as well as improved stability of the output laser power. These changes improved the measurement of spectral reflectance properties of plants and provided reliable discrimination of three different broadleaved plants using only three narrow wavelength bands. A field trial with the final prototype demonstrated successful discrimination of these three different plants at 5 km/h when a shroud was used to block ambient light. A survey of spectral reflectance of four crops (sugarcane, cotton, wheat and sorghum) and the weeds growing amongst these crops was conducted to determine the potential for use of the prototype weed sensor to control spot-spraying of herbicides. Visible reflectance spectra were recorded from individual leaves using a fibre spectrometer throughout the growing season for each crop. A discriminant analysis was conducted based on six narrow wavebands extracted from leaf level spectral reflectance measured with a spectrometer. The analysis showed the potential to discriminate cotton and sugarcane fro

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights

Development of a new GIS-based method to detect high natural value farmlands. A case study in central Italy

An original method for the identification of High Natural Value farmlands is presented. Gathering information about land use (CORINE Land Cover), geomorphology (elevation and Terrain Ruggedness Index) and remote sensing data in a GIS environment we were able to develop a new detection process; its application to a wide sector of central Italy, in areas characterized by high biodiversity and relevant agronomic and cultural value, is presented. Thus, a new tool for diminishing sampling efforts and economic and time wastes in territorial studies is provided