Ideally, integrated pest management should rely on an array of tactics. In reality, the main technologies in use are synthetic pesticides. Because of well-documented problems with reliance on synthetic pesticides, viable alternatives are sorely needed. Physical controls can be classified as passive (e.g., trenches, fences, organic mulch, particle films, inert dusts, and oils), active (e.g., mechanical, polishing, pneumatic, impact, and thermal), and miscellaneous (e.g., cold storage, heated air, flaming, hot-water immersion). Some physical methods such as oils have been used successfully for preharvest treatments for decades. Another recently developed method for preharvest situations is particle films. As we move from production to the consumer, legal constraints restrict the number of options available. Consequently, several physical control methods are used in postharvest situations. Two noteworthy examples are the entoleter, an impacting machine used to crush all insect stages in flour, and hot-water immersion of mangoes, used to kill tephritid fruit fly immatures in fruit. The future of physical control methods will be influenced by sociolegal issues and by new developments in basic and applied research.

Seedbank studies often suffer from major methodological inadequacies such as absence of appropriate statistical data analysis and low sampling intensity. Multivariate analysis and computer mapping are innovative ways to treat seedbank data. Computer contour mapping was used to visualize spatial patterns of a population of common lambsquarters at three intervals during a growing season. At one site, high spring seed density of 600 000 seed m-2 was decreased to 18.3% of its original size by July, while at another site, low spring seedbank of common lambsquarters of 25 000 seed m-2 increased to 40 000 seed m-2 by autumn. Seedbank studies usually report results on total seed density or on densities of the most abundant species because of difficulties in analyzing large species matrices using parametric statistics. Multivariate analysis and specifically canonical discriminant analysis (CDA) are well suited for seedbank populations. The seedbanks of six agricultural habitats were demonstrated to be floristically different based on the analysis of the relative abundance of weed species in each site using CDA. Organic soils either under grassland or cultivated had significantly larger total seedbanks than mineral soils. If seedbanks are to be used in predictive population models, quantitative data that are reliable, rapidly obtained with limited resources, and logistically feasible for large sampling protocols are needed. Image analysis may be a potential rapid technique for weed seed recognition of washed soil samples.

Real-time spot spraying of weed patches requires the development of sensors for the automatic detection of weeds within a crop. In this context, the potential of UVinduced fluorescence of green plants for corn-weed discrimination was evaluated. A total of 1 440 spectral signatures of fluorescence were recorded in a greenhouse from three plant groups (four corn hybrids, four dicotyledonous weed species and four monocotyledonous weed species) grown in a growth chamber. With multi-variate analysis, the full information contained in each spectrum was first reduced to the scores calculated from five principal components. Then, a linear discriminant analysis was applied on these scores to classify spectra on a species/hybrids basis and, subsequently, the resulting classes were aggregated according to the three plant groups. This two-step process minimized the error generated by heterogeneous groups such as dicotyledonous weeds. The output of this classification shows the significant potential of UV-induced fluorescence for plant group discrimination as the success rate reached 91.8%. No error was observed between corn and dicot weeds and most of the errors between corn and grasses came from confusion between the hybrid Pioneer 39Y85 and Setaria glauca L. (Beauv.). Analysis also determined that the position of the fluorescence sensor on the leaf and the plant age had negligible effects on the efficiency of fluorescence to discriminate plant groups. The factors to consider for transferring the results about UV-induced fluoro-sensing from laboratory to the field are discussed.