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Background A large population of Paropsisterna beata (eucalyptus leaf beetle) was detected on Eucalyptus nitens (H. Deane & Maiden) Maiden (Myrtaceae) at Whitemans Valley, a suburb east of Upper Hutt, Wellington, in 2012. The suburb is a semi-rural residential area with a large number of eucalypt, planted for amenity, shelterbelt and firewood. Surveillance to delimit spread showed that the beetle population was confined to about 0.7 ha consisting of about 40 eucalypts. The Ministry for Primary Industries (MPI) initiated a response to eradicate the beetle population. Findings Aerial applications of Dominex EC 100 (alpha-cypermethrin) and ground applications of Talstar (bifenthrin) respectively over a 15-month period targeted the adults and larvae in the foliage and the pre-pupae, larvae and emerging adults in the leaf litter. Removal of overwintering habitat by stripping loose bark from host trees further reduced the beetle population. Following these treatments, the beetle has not been detected through a series of surveys using light traps, bark inspection, sticky tapes, visual inspection from the ground, climbing and felling host trees for inspection for 2 years since the last detection of two adults on neighbouring trees. Conclusions The P. beata population has been successfully eradicated using a combination of aerial and ground-based application of insecticides. The use of precision aerial applications (spot-spraying) has provided an additional tool for incursion response.

Weeds pose a persistent threat to farmers’ yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to the surrounding ecosystems. Precision spraying aims to reduce harms to the surrounding environment by targeting only the weeds rather than spraying the entire field with herbicide. Such an approach requires weeds to first be detected. With the advent of convolutional neural networks, there has been significant research trialing such technologies on datasets of weeds and crops. However, the evaluation of the performance of these approaches has often been limited to the standard machine learning metrics. This paper aims to assess the feasibility of precision spraying via a comprehensive evaluation of weed detection and spraying accuracy using two separate datasets, different image resolutions, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is proposed to compare the performance of different detection algorithms while varying the precision of the spray nozzles. The key performance indicators in precision spraying that this study focuses on are a high weed hit rate and a reduction in herbicide usage. This paper introduces two metrics, namely, weed coverage rate and area sprayed, to capture these aspects of the real-world performance of precision spraying and demonstrates their utility through experimental results. Using these metrics to calculate the spraying performance, it was found that 93% of weeds could be sprayed by spraying just 30% of the area using state-of-the-art vision methods to identify weeds.

Background Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides. Results GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F 1 scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated. Conclusion These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers.

Spot spraying applications offer the opportunity to target specific weeds in a field, while simultaneously reducing herbicide usage and increasing the long‐term efficacy of chemical control options. The study is focused on controlling California weedy rice accessions (Oryza spp.) and problematic grass weeds with a spot spray application of clethodim in a flooded rice system. The efficacy of incorporating nonionic surfactant to clethodim applications was also assessed. Dose‐response experiments were carried out in a greenhouse on five weedy rice accessions, common grass rice weeds, and cultivated rice varieties L207, M105, M206, M209, M211, and S102 to determine the dose needed to affect these populations. Clethodim was applied in a field setting to assess spot spraying efficacy, the possibility of herbicide dispersion in the water, and crop injury. Clethodim successfully controlled weedy rice and grasses in the greenhouse. The effective rates to control 90% of the five test populations (ED90) were between 51 and 74 g ai ha−1 clethodim for weedy rice accessions. Adding nonionic surfactant to clethodim increased its efficacy by 1.6‐ to 1.9‐fold. Cultivated rice varieties did not exhibit any tolerance to clethodim, however, spot spraying applications at 150 g ai ha−1 clethodim did not cause any dispersion in the field. Clethodim spot spray application was effective both at the three‐ to four‐leaf growth stage and tillering growth stage for weedy rice. Core Ideas Spot spray herbicide application successfully controlled weedy rice. Adding nonionic surfactant (NIS) increased the efficacy of clethodim. Clethodim at labeled rate did not cause rice injury outside the spot spray area in rice field. Clethodim spot spray was effective both at the three to four‐leaf growth stage and tillering growth stage for weedy rice.

Weed encroachment negatively affects pasture productivity by reducing herbage allowance, stocking rates, and livestock performance. Amaranthus spinosus L. is a weed species widely found in pastures worldwide and is considered challenging for ranchers due to its great potential for invasion, making it difficult to control. The high costs of chemical application and the global concern about environmental impacts restrict indiscriminate herbicide spraying in pastures. Site‐specific weed management (SSWM) is a weed management strategy based on weed spot‐spraying that has the potential to overcome these issues. Images from unmanned aerial vehicles (UAVs) can provide valuable information for weed mapping to drive the herbicide application in pastures. Deep learning techniques have been highlighted in image classification tasks. We developed a deep convolutional neural network (CNN)‐based image segmentation model based on the U‐Net architecture to detect and map Amaranthus spinosus in bermudagrass pastures using red–green–blue images acquired through UAV flying in moderate‐high altitude. The images were acquired from twelve paddocks under three treatments (weed‐free, weed‐strips, or weed‐infested) during the summer (2021‐2022). The CNN model was able to detect around 80% of the A. spinosus with an average prediction accuracy of 94%. Our weed mapping showed the potential of using the U‐Net model to generate a herbicide application map to be inserted into the sprayer system, reducing up to 76% of the amount of herbicide applied. Further studies are encouraged to increase the robustness of the model across species and development stages and develop sprayer systems to implement the spot‐spraying in field conditions. Core Ideas Low‐cost red–green–blue images obtained by drones are useful for weed detection in pastures. Deep convolutional neural network can detect up to 80% of Amaranthus spinosus L. in pastures accurately. Remote sensing–based weed mapping enables site‐specific weed management in pastures. Spot‐spraying of herbicides reduces weed control costs by up to 40% in heavily infested pastures. Site‐specific weed management is a promising tool to mitigate the environmental impacts of grazing systems.

Spot spraying POST herbicides is an effective approach to reduce herbicide input and weed control cost. Machine vision detection of grass or grass-like weeds in turfgrass systems is a challenging task due to the similarity in plant morphology. In this work, we explored the feasibility of using image classification with deep convolutional neural networks (DCNN), including AlexNet, GoogLeNet, and VGGNet, for detection of crabgrass species (Digitaria spp.), doveweed [Murdannia nudiflora (L.) Brenan], dallisgrass (Paspalum dilatatum Poir.), and tropical signalgrass [Urochloa distachya (L.) T.Q. Nguyen] in bermudagrass [Cynodon dactylon (L.) Pers.]. VGGNet generally outperformed AlexNet and GoogLeNet in detecting selected grassy weeds. For detection of P. dilatatum, VGGNet achieved high F1 scores (≥0.97) and recall values (≥0.99). A single VGGNet model exhibited high F1 scores (≥0.93) and recall values (1.00) that reliably detected Digitaria spp., M. nudiflora, P. dilatatum, and U. distachya. Low weed density reduced the recall values of AlexNet at detecting all weed species and GoogLeNet at detecting Digitaria spp. In comparison, VGGNet achieved excellent performances (overall accuracy = 1.00) at detecting all weed species in both high and low weed-density scenarios. These results demonstrate the feasibility of using DCNN for detection of grass or grass-like weeds in turfgrass systems.

In New Zealand, pastoral farming for dairy and meat production is the major land use. As with any agricultural production system, weeds are a threat to efficient pasture production in New Zealand. In this review, we outline the problems caused by weeds in New Zealand pastures, and the management strategies being used to control them. There are currently 245 plant species from 40 plant families that are considered to be troublesome weeds in New Zealand pastures. The application of herbicides is an important approach to manage weeds in New Zealand pastures; however, a key to the success of these pastures is the use of clovers in combination with the grasses, so the challenge is to find herbicides that selectively control weeds without damaging these legumes. The use of spot spraying and weed wiping are often required to ensure selective control of some weed species in these pastures. Non-chemical agronomic approaches such as grazing management and using competitive pasture species often play a more important role than herbicides for weed management in many New Zealand pastures. Thus, integrated weed management using a combination of herbicides and good pasture management strategies leads to the most cost-effective and efficient control of pasture weeds in New Zealand.

Spot spraying can significantly reduce herbicide use while maintaining equal weed control efficacy as a broadcast application of herbicides. Several online spot-spraying systems have been developed, with sensors mounted on the sprayer or by recording the RTK-GNSS position of each crop seed. In this study, spot spraying was realized offline based on georeferenced unmanned aerial vehicle (UAV) images with high spatial resolution. Studies were conducted in four maize fields in Southwestern Germany in 2023. A randomized complete block design was used with seven treatments containing broadcast and spot applications of pre-emergence and post-emergence herbicides. Post-emergence herbicides were applied at 2–4-leaf and at 6–8-leaf stages of maize. Weed and crop density, weed control efficacy (WCE), crop losses, accuracy of weed classification in UAV images, herbicide savings and maize yield were measured and analyzed. On average, 94% of all weed plants were correctly identified in the UAV images with the automatic classifier. Spot-spraying achieved up to 86% WCE, which was equal to the broadcast herbicide treatment. Early spot spraying saved 47% of herbicides compared to the broadcast herbicide application. Maize yields in the spot-spraying plots were equal to the broadcast herbicide application plots. This study demonstrates that spot-spraying based on UAV weed maps is feasible and provides a significant reduction in herbicide use.

This paper studied the atomization characteristics of different spray nozzles under the spot spraying method and designed a test system for the atomization characteristics. First, the effective spray height range was determined based on the effective droplet size of 106–403 μm, the spray height of 200–500 mm, the operating speed of 0.5–1 m/s, and the droplet size requirements. The effective height ranges of the HVV25-02, HVV40-02, and HVV50-02 nozzles are 277–500 mm, 200–426 mm, and 200–266 mm, respectively. Second, the influences of pressure, the opening time of the solenoid valve, and the nozzle aperture on the atomization characteristics were studied through experiment. The experiment was repeated three times, with 10,000 points monitored each time. The test results show that the droplet size of spot spraying decreases with the increase in pressure, while the droplet velocity and droplet distribution relative span have no correlation with pressure. With the increase in the opening time of the solenoid valve, the droplet size does not change regularly, the droplet velocity generally shows an upward trend, and the droplet distribution relative span (RS) value decreases gradually. With the increase in the nozzle aperture, both droplet size and droplet velocity increase, and the distribution span shows a trend of first increasing and then decreasing. The droplet velocity of spot spraying is 4.1 m/s lower than that of continuous spraying, on average, and the droplet distribution relative span value is 2.2 higher than that of continuous spraying. This research can provide a basis and reference for the selection of appropriate spot spraying operation parameters.

1. Globally the prevalence and impact of invasive non-native plant species is increasing rapidly. Experimentally based research aimed at supporting management is limited in its ability to keep up with this pace, partly because of the importance of understanding historical abiotic and biotic conditions. Contrastingly, landholders are in unique positions to witness species turnover in grasslands, adapt management practices in response and learn from successes and failures. 2. This local knowledge could be crucial for identifying feasible solutions to land degradation, and ecological restoration, but local knowledge is rarely explicitly embedded in ecological research. 3. We use a sequential exploratory strategy where we first interview (semi-directive approach) 15 landholders within the Bega region of New South Wales, Australia concerning the changing ecological characteristics of both extensively and intensively managed grassy woodlands and perceived impacts following arrival of the invasive exotic introduced species, African lovegrass (ALG), Eragrostis curvula. 4. Based on the results of these interviews, we then conducted a field study where we tested 7 landholder-generated hypotheses at 57 sites. 5. The field study validated many of the landholder management perceptions including: ALG was negatively correlated with species richness, canopy cover and dominant grasses like Themeda triandra. Mechanical slashing increased exotic ALG abundance. The prevalence of ALG in the soil seed bank was positively correlated with its abundance above-ground. Study observations that contradicted landholder perceptions included: ALG was not more palatable nor did its abundance decline in response to increasing soil fertility. Spot spraying with herbicides was effective at controlling abundance, despite its reputation as ineffective. Landholder observations also highlighted key hypotheses concerning modes of spread that require long-term studies including the roles of drought and overgrazing. 6. Synthesis and applications. Overall, we found local knowledge coupled with scientific methods can act in tandem as a highly effective approach for developing management recommendations. This approach identifies local perceptions that are not substantiated by scientific data to halt potentially harmful practices, and observations that are insightful predictions about the dynamics and impacts of non-native species that need long-term experiments to corroborate scientifically.