A potential relationship between soil disinfestation efficacy and leaf green reflectance.

Soil disinfestation with steam was evaluated as an alternative to fumigation. Following soil disinfestation, plant health has traditionally been measured using plant size and yield. Plant health can be measured in a timely manner more efficiently, more easily and non-destructively using image analysis. We hypothesized that plant health could be quantified and treatments can be differentiated using an RGB (Red, Green, Blue) image analysis program, particularly by observing the greenness of plant leaves. However, plant size or the proportion of green area could be unreliable due to plant loss and camera's position and angle. To this end, we decided to evaluate plant health by analyzing the RGB codes associated with the green color only, which detects the chlorophyll reflectance and nutrient status, noting that the degree of greenness within the green-leaf-area was not affected by the plant size. We identified five RGB codes that are commonly observed in the plant leaves and ordered them from dark green to light green. Among the five RGB codes, the relative percentage covered by the darkest green to the lightest green was significantly different between the steam and chloropicrin treatments and the control, and it was not significantly different between the steam and chloropicrin treatments. Furthermore, the result was correlated with the total yield, and the trend observed in the first year was replicated in the second year of this experiment. In this study, we demonstrate that the RGB image analysis can be used as an early marker of the treatment effect on the plant health and productivity.

Incorporating statistical strategy into image analysis to estimate effects of steam and allyl isocyanate on weed control.

Weeds are the major limitation to efficient crop production, and effective weed management is necessary to prevent yield losses due to crop-weed competition. Assessments of the relative efficacy of weed control treatments by traditional counting methods is labor intensive and expensive. More efficient methods are needed for weed control assessments. There is extensive literature on advanced techniques of image analysis for weed recognition, identification, classification, and leaf area, but there is limited information on statistical methods for hypothesis testing when data are obtained by image analysis (RGB decimal code). A traditional multiple comparison test, such as the Dunnett-Tukey-Kramer (DTK) test, is not an optimal statistical strategy for the image analysis because it does not fully utilize information contained in RGB decimal code. In this article, a bootstrap method and a Poisson model are considered to incorporate RGB decimal codes and pixels for comparing multiple treatments on weed control. These statistical methods can also estimate interpretable parameters such as the relative proportion of weed coverage and weed densities. The simulation studies showed that the bootstrap method and the Poisson model are more powerful than the DTK test for a fixed significance level. Using these statistical methods, three soil disinfestation treatments, steam, allyl-isothiocyanate (AITC), and control, were compared. Steam was found to be significantly more effective than AITC, a difference which could not be detected by the DTK test. Our study demonstrates that an appropriate statistical method can leverage statistical power even with a simple RGB index.

Robotic weeders can improve weed control options for specialty crops.

Specialty crop herbicides are not a priority for the agrochemical industry, and many of these crops do not have access to effective herbicides. High-value fruit and vegetable crops represent small markets and high potential liability in the case of herbicide-induced crop damage. Meanwhile, conventional and organic specialty crop producers are experiencing labor shortages and higher manual weeding costs. Robotic weeders are promising new weed control tools for specialty crops, because they are cheaper to develop and, with fewer environmental and human health risks, are less regulated than herbicides. Now is the time for greater investment in robotic weeders as new herbicides are expensive to develop and few in number, organic crops need better weed control technology and governments are demanding reduced use of pesticides. Public funding of fundamental research on robotic weeder technology can help improve weed and crop recognition, weed control actuators, and expansion of weed science curricula to train students in this technology. Robotic weeders can expand the array of tools available to specialty crop growers. However, the development of robotic weeders will require a broader recognition that these tools are a viable path to create new weed control tools for specialty crops. © 2019 Society of Chemical Industry.

Evaluation of Steam and Soil Solarization for Meloidogyne arenaria Control in Florida Floriculture Crops.

Steam and soil solarization were investigated for control of the root-knot nematode Meloidogyne arenaria in 2 yr of field trials on a commercial flower farm in Florida. The objective was to determine if preplant steam treatments in combination with solarization, or solarization alone effectively controlled nematodes compared to methyl bromide (MeBr). Trials were conducted in a field with naturally occurring populations of M. arenaria. Treatments were solarization alone, steam treatment after solarization using standard 7.6-cm-diameter perforated plastic drain tile (steam 1), steam treatment following solarization using custom-drilled plastic drain tile with 1.6-mm holes spaced every 3.8 cm (steam 2), and MeBr applied at 392 kg/ha 80:20 MeBr:chloropicrin. Drain tiles were buried approximately 35 cm deep with four tiles per 1.8 by 30 m plot. Steam application followed a 4-wk solarization period concluding in mid-October. All steam was generated using a Sioux propane boiler system. Plots were steamed for sufficient time to reach the target temperature of 70°C for 20 min. Solarization plastic was retained on the plots during steaming and plots were covered with a single layer of carpet padding to provide additional insulation. The floriculture crops larkspur (Delphinium elatum and Delphinium × belladonna), snapdragon (Antirrhinum majus), and sunflower (Helianthus annuus) were produced according to standard commercial practices. One month after treatment in both years of the study, soil populations of M. arenaria were lower in both steam treatments and in MeBr compared to solarization alone. At the end of the season in both years, galling on larkspur, snapdragon, and sunflowers was lower in both steam treatments than in solarization. Both steam treatments also provided control of M. arenaria in soil at the end of the season comparable to, or exceeding that provided by MeBr. Both steam treatments also reduced M. arenaria in snapdragon roots comparable to, or exceeding control with MeBr. Meloidogyne arenaria in soil increased in solarization alone. Solarization alone also had higher gall ratings on larkspur, snapdragon, and sunflower than all other treatments. Steam provided excellent control of M. arenaria in this study.

Mustard and Other Cover Crop Effects Vary on Lettuce Drop Caused by Sclerotinia minor and on Weeds.

Mustard cover crops have been suggested as a potential biofumigant for managing soilborne agricultural pests and weeds. We conducted several experiments in commercial lettuce fields in the Salinas Valley, CA, to evaluate the effects of mustard cover crops on lettuce drop caused by Sclerotinia minor and on weed density and seed viability. In a long-term study, we measured the effects of white and Indian mustard cover crops on the density of S. minor sclerotia in soil, lettuce drop incidence, weed densities, weed seed viability, and crop yield in head lettuce. We also tested broccoli and rye cover crop treatments and a fallow control. Across several short-term studies, we evaluated the density of S. minor sclerotia in soil, lettuce drop incidence, weed densities, and weed seed viability following cover cropping with a mustard species blend. Numbers of sclerotia in soil were low in most experimental locations and were not affected by cover cropping. Mustard cover crops did not reduce disease incidence in the long-term experiment but the incidence of lettuce drop was lower in mustard-cover-cropped plots across the short-term experiments. With the exception of common purslane and hairy nightshade, weed densities and weed seed viability were not significantly reduced by cover cropping with mustard. Head lettuce yield was significantly higher in mustard-cover-cropped plots compared with a fallow control. Glucosinolate content in the two mustard species was similar to those measured in other studies but, when converted to an equivalent of a commercial fumigant, the concentrations were much lower than the labeled rate for lettuce production. Although mustard cover cropping resulted in yield benefits in this study, there was little to no disease or weed suppression.