A classification methodology for the risk of weed infestation using fuzzy logic

Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.     

Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying

Information on temporal and spatial variation in weed seedling populations within agricultural fields is very important for weed population assessment and management. Primarily, spatial information allows a potential reduction in herbicide use, when post‐emergent herbicides are only applied to field sections with high weed infestation levels. This paper presents a system for site‐specific weed control in sugar beet, maize, winter wheat, winter barley, winter rape and spring barley. The system includes on‐line weed detection using digital image analysis, computer‐based decision making and Global Positioning System‐controlled patch spraying. In a 2‐year study, herbicide use with this map‐based approach was reduced in winter cereals by 6–81% for herbicides against broad leaved weeds and 20–79% for grass weed herbicides. Highest savings were achieved in cereals followed by sugar beet, maize and winter rape. The efficacy of weed control varied from 85% to 98%, indicating that site‐specific weed management will not result in higher infestation levels in the following crops.     

Tolerance of four spring barley (Hordeum vulgare) varieties to weed harrowing

We investigated the tolerance to weed harrowing of four spring barley varieties and examined the possible interactions between varietal weed suppressive ability and two nutrient levels. Tolerance was defined as the combined effect of crop resistance (ability to resist soil covering) and crop recovery (the ability to recover in terms of yield). The weed harrowing strategy was a combination of one pre‐ and one post‐emergence weed harrowing. In terms of yield, the four varieties responded significantly differently to weed harrowing and the response depended on nutrient level. At the lower nutrient level, weed harrowing caused an increase in yield of 4.4 hkg ha⁻¹ for a strong competitor (cv. Otira), while there was no effect on yield at the higher nutrient level. For a weaker competitor (cv. Brazil), weed harrowing caused no change in yield at the lower nutrient level, whereas yield decreased by 6.0 hkg ha⁻¹ at the higher nutrient level. There were marked differences between the weed suppressive ability of the four varieties when not harrowed, with less pronounced but significant differences when harrowed. Weed harrowing did not change the weed suppressive ability of a variety. Varieties that are tall at post‐emergence harrowing and have increased density after pre‐emergence harrowing, are the ones that benefit most from weed harrowing.     

Investigating the selectivity of weed harrowing with new methods

In six field experiments on post‐emergence weed harrowing in spring barley, the effects of row spacing, timing, direction and orientation on crop/weed selectivity were investigated. The efficacies of increasing intensities of harrowing generated either by increasing number of passes or increasing driving speed were also tested. Selectivity was defined as the relationship between crop burial in soil immediately after treatment and weed control. To estimate crop burial, digital image analysis was used in order to make objective estimations. The study showed that narrow row spacing decreased selectivity in a late crop growth stage, whereas row spacing in the range 5.3–24 cm had no effects at an early growth stage. Harrowing across rows decreased selectivity in one out of two experiments. Whether repeated passes with the harrow were carried out in the same orientation along the rows or in alternative orientations forth and back was unimportant. There were indications that a high harrowing intensity produced by a single pass at high speed gave a lower selectivity than a similar intensity produced by several passes at a low speed. Impacts on selectivity, however, were small and only significant at high degrees of weed control. Timing had no significant impact on selectivity.     

Assessment of leaf cover and crop soil cover in weed harrowing research using digital images

Objective assessment of crop soil cover, defined as the percentage of leaf cover that has been buried in soil because of weed harrowing, is crucial to further progress in post‐emergence weed harrowing research. Up to now, crop soil cover has been assessed by visual scores, which are biased and context‐dependent. The aim of this study was to investigate whether digital image analysis is a feasible method to estimate crop soil cover in the early growth stages of cereals. Two main questions were examined: (i) how to capture suitable digital images under field conditions with a standard high‐resolution digital camera and (ii) how to analyse the images with an automated digital image analysis procedure. The importance of light conditions, camera angle, size of recorded area, growth stage and direction of harrowing were investigated, in order to establish a standard for image capture and an automated image analysis procedure based on the excess green colour index was developed. The study shows that the automated digital image analysis procedure provided reliable estimations of leaf cover, defined as the proportion of pixels in digital images determined to be green, which were used to estimate crop soil cover. A standard for image capture is suggested and it is recommended that digital image analysis be used to estimate crop soil cover in future research. The prospects of using digital image analysis in future weed harrowing research are discussed.     

Automatic sex detection of individuals of Ceratitis capitata by means of computer vision in a biofactory

BACKGROUND: The sterile insect technique (SIT) is acknowledged around the world as an effective method for biological pest control of Ceratitis capitata (Wiedemann). Sterile insects are produced in biofactories where one key issue is the selection of the progenitors that have to transmit specific genetic characteristics. Recombinant individuals must be removed as this colony is renewed. Nowadays, this task is performed manually, in a process that is extremely slow, painstaking and labour intensive, in which the sex of individuals must be identified. The paper explores the possibility of using vision sensors and pattern recognition algorithms for automated detection of recombinants. RESULTS: An automatic system is proposed and tested to inspect individual specimens of C. capitata using machine vision. It includes a backlighting system and image processing algorithms for determining the sex of live flies in five high-resolution images of each insect. The system is capable of identifying the sex of the flies by means of a program that analyses the contour of the abdomen, using fast Fourier transform features, to detect the presence of the ovipositor. Moreover, it can find the characteristic spatulate setae of males. Simulation tests with 1000 insects (5000 images) had 100% success in identifying male flies, with an error rate of 0.6% for female flies. CONCLUSION: This work establishes the basis for building a machine for the automatic detection and removal of recombinant individuals in the selection of progenitors for biofactories, which would have huge benefits for SIT around the globe.     

Assessing regional scale weed distributions, with an Australian example using Nassella trichotoma

Knowledge of the spatial distribution of weed infestations over regional scales is essential for effective management of source populations and to assess future threats. To this end, the distributions of Nassella trichotoma across a study area in south‐east New South Wales, Australia, were analysed using the geographically local Getis–Ord G_i* spatial hotspot clustering statistic. The clustering of N. trichotoma observations was analysed at three infestation levels: presence (at any density), patch level and the occasional plant level. The results indicate that there are c. 578 km² of cells containing N. trichotoma in strongly clustered infestations, 11.2 km² within weakly clustered infestations distinct from the main clusters, and 55 km² that are not clustered. There are 117 km² of strongly clustered patch level cells, 3 km² in distinct but weak clusters, and none outside of a cluster area. Of the occasional plant level cells, 329 km² are strongly clustered, 6.2 km² are in distinct but weak clusters, and 19 km² are not clustered. These results provide a mechanism by which control efforts can be prioritized. The analysis approach described in this paper provides a consistent, quantitative and repeatable approach to assess weed infestations across regional scales and can be applied to any weed species for which spatial distribution data are available.     

Using a competition model to quantify the optimal trade-off between machine vision capability and weed removal effectiveness

The algorithm of an optical detection system was first investigated for its ability to correctly classify transplanted crops and weeds during the critical early stages of crop establishment and its robustness over a range of different crop species. The trade-off was then examined between increasing the sensitivity of the detection system vs. the possibility of, in doing so, misclassifying some crop plants as weeds and inadvertently removing them. This was achieved by running a competition model using parameters derived from the image analysis and assessing the outcome of scenarios in terms of yield. The optimum parameter values to maximize the detection of the crop and the optimum parameter values to maximize the detection of the weed appeared relatively insensitive to time of image capture or weed density. They also appeared insensitive for different crop species where the crop had similar growth habit. However, competition scenarios indicated that the detection system parameter settings to achieve optimum yields were sensitive to the competitive ability of the weed species. For Veronica persica, crop yield was more sensitive to accidental crop removal than from competition. In contrast, in the presence of Tripleurospermum inodorum, yield loss was more attributable to weed competition. Importantly, linking the detection system with the competition model illustrated the principle that optimum yield may not necessarily be obtained by maximizing weed removal or minimizing crop removal. This first example of combining a detection system with a competition model presents a new opportunity to quantify the sensitivity of image classification in terms of yield.     

In‐field classification of herbicide‐resistant Papaver rhoeas and Stellaria media using an imaging sensor of the maximum quantum efficiency of photosystem II

Reliable in‐season and in‐field tools for rapidly quantifying herbicide efficacy in dicotyledonous weeds are missing. In this study, the maximum quantum efficiency of photosystem II (F_v/F_m) of susceptible and resistant Papaver rhoeas and Stellaria media populations in response to treatments with acetolactate synthase (ALS) inhibitors were examined. Seedlings (4–6 leafs) were transplanted into the field immediately after the application of the ALS inhibitors florasulam, metsulfuron‐methyl and tribenuron‐methyl. The F_v/F_m values were assessed 1–7, 9 and 14 days after treatment (DAT). Based on the F_v/F_m values of all fluorescing pixels in the images of herbicide‐treated plants, discriminant maximum‐likelihood classifiers were created. Based on this classifier, an independent set of images were classified into ‘susceptible’ or ‘resistant’ plants. The classifiers’ accuracy, false‐positive rate and false‐negative rate were calculated. The F_v/F_m values of sensitive P. rhoeas and S. media plants decreased within 3 DAT by 28–43%. The F_v/F_m values of the resistant plants of both species were 20% higher than those of the sensitive plants in all herbicide treatments. The classifier separated sensitive and resistant plants 3 DAT with accuracies of 62–100%. False‐positive and false‐negative classifications decreased with increasing DAT. We conclude that by the assessment of the F_v/F_m value in combination with the classification sensitive and resistant P. rhoeas and S. media populations could be separated 3 DAT. This technique can help to select effective control methods and speed up the monitoring process of susceptible and resistant weeds.     

Plant MetGenMAP数据库分析水稻表达谱芯片数据的方法初探

基因芯片数据分析是利用基因芯片技术进行各项研究的关键。本文以水稻Affymetrix表达谱芯片的数据为例,利用Plant MetGenMAP数据库,对水稻受病原菌侵染后基因表达量及代谢途径的变化情况进行了初步分析,同时,提供了一种利用Plant MetGenMAP数据库分析水稻基因芯片数据的方法。