Ran Nisim Lati

Robust Methods for Measurement of Leaf-Cover Area and Biomass from Image Data

Leaf-cover area is a widely required plant development parameter for predictive models of weed growth and competition. Its assessment is performed either manually, which is labor intensive, or via visual inspection, which provides biased results. In contrast, digital image processing enables a high level of automation, thereby offering an attractive means for estimating vegetative leaf-cover area. Nonetheless, image-driven analysis is greatly affected by illumination conditions and camera position at the time of imaging and therefore may introduce bias into the analysis. Addressing both of these factors, this paper proposes an image-based model for leaf-cover area and biomass measurements. The proposed model transforms color images into an illumination-invariant representation, thus facilitating accurate leaf-cover detection under varying light conditions. To eliminate the need for fixed camera position, images are transformed into an object–space reference frame, enabling measurement in absolute metric units. Application of the proposed model shows stability in leaf-cover detection and measurement irrespective of camera position and external illumination conditions. When tested on purple nutsedge, one of the worlds most troublesome weeds, a linear relation between measured leaf-cover area and plant biomass was obtained regardless of plant developmental stage. Data on the expansion of purple nutsedge leaf-cover area is essential for modeling its spatial growth. The proposed model offers the possibility of acquiring reliable and accurate biological data from digital images without extensive photogrammetric or image-processing expertise. Nomenclature: Purple nutsedge, Cyperus rotundus L. CYPRO

Temperature- and Radiation-Based Models for Predicting Spatial Growth of Purple Nutsedge (Cyperus rotundus)

Abstract Purple nutsedge is a troublesome C4 weed, characterized by high photosynthetic efficiency, compared to C3 weeds. As its dispersal is based on vegetative growth, accurate prediction of its growth could help in arriving at favorable management decisions. This article details the development and validation of predictive models of purple nutsedge spatial growth, based on temperature (thermal model), and temperature and radiation (photothermal model) measurements. Plants were grown in six experiments in the summers of 2008, 2009, and 2010, under different temperature and radiation conditions. Results indicate that under optimal temperatures, radiation becomes the main growth-limiting factor, and is highly related to the final leaf-cover area (R2  =  0.89). Comparison of the thermal and photothermal models showed that under all conditions, including varied temperature and radiation, the photothermal model performs significantly better, with differences in root-mean-square error values reaching up to 0.073, compared to 0.195 with the thermal model. Validation experiments confirmed the ability of the photothermal model to predict purple nutsedge spatial growth accurately. Nomenclature: Purple nutsedge, Cyperus rotundus L. CYPRO.

Effect of Tuber Density and Trifloxysulfuron Application Timing on Purple Nutsedge (Cyperus rotundus) Control

Abstract Herbicides are the basis for conventional management of purple nutsedge, one of the worlds most troublesome weeds. However, as concern rises over their environmental impact, farmers are being required to reduce herbicide usage. Herbicide efficacy is strongly affected by weed growth stage and density at application, and when herbicides are applied under optimal conditions, low rates can provide maximal control efficacy (CE). Therefore, this study aimed to determine the time window for control of purple nutsedge using a low rate of herbicide, based on an effective degree days (EDDs) model, at low (one tuber) and high (10 tubers) densities. Two experiments were performed under field conditions, in the summers of 2009 and 2010. Rate of 3.75 g a.i. ha−1 trifloxysulfuron was applied once on each of five individual application dates. The growth of both treated and untreated plots was evaluated by means of leaf cover area (LCA) and biomass, which were then used to establish the time window for control. Results showed differences in both growth parameters between low and high tuber densities. The high-density patches reached LCA and fresh biomass values of 1,367 g and 1.12 m2, respectively, compared to 604 g and 0.69 m2, respectively, in the lower density patches. The favorable control periods based on biomass and LCA for the lower density patches were set to later dates than those for the higher density patches, 626 EDD compared to 483 EDD for biomass, and 786 EDD compared to 502 EDD for LCA, respectively. Although differences between the biomass- and LCA-based favorable control periods were observed at both tuber densities, the computed linear relations between the two growth parameters enabled adjusting them and setting the appropriate control period. Nomenclature: Trifloxysulfuron; Purple nutsedge, Cyperus rotundus L.; CYPRO.

Three-dimensional image-based modelling of linear features for plant biomass estimation

Biomass estimation is important for biological research and agricultural management. Low-cost two-dimensional (2D) computer vision has been applied to non-contact biomass estimation. However, the rapid increase of computing power has enabled the use of stereo vision models for this purpose. The objectives of this study were to develop an alternative 3D image-based reconstruction model that utilizes geometric features of plant leaves for estimation of biomass and to evaluate its robustness to varying illumination conditions, complex plant geometry, and leaf surface texture. At its core, the proposed model extracts and matches linear features that are then reconstructed in 3D space. As linear features characterize the plants silhouette and entities on its surface, a detailed and accurate 3D reconstruction of its shape can be obtained. The algorithm performance was evaluated both in greenhouse and field studies, showing accurate estimation under varying canopy geometries, growth stages, and illumination conditions. Results show an ability to accurately estimate height (error ˜4.5%) and leaf cover area (error ˜4.5%) values under these conditions. Additionally, a strong linear relation was obtained between estimated plant volume and measured biomass (R 2 ˜ 0.92), which proved to be an accurate predictor when applied on new plants (error ˜4.5%). These abilities make this model a promising tool for future development of biological models and precision agricultural practices.

Light Intensity Is a Main Factor Affecting Fresh Market Spinach Tolerance for Phenmedipham

Abstract The few available herbicides for fresh market spinach do not provide adequate weed control, and there is need for additional herbicide tools. Phenmedipham is registered for use in processing spinach but not in fresh spinach, because of potential injury and the short interval between application and spinach harvest. The objectives of this study were to evaluate the tolerance level of fresh spinach varieties to phenmedipham and evaluate the impact of light intensity on tolerance of spinach to phenmedipham. In the greenhouse, nine spinach varieties were treated with phenmedipham (0.55 kg ai ha−1). Spinach varieties exhibited a wide range of tolerance, and dry weights of treated plants ranged from 40 to 78% compared to the nontreated control. Based on the phenmedipham tolerance screen, two varieties with low (Nordic) and high (Regal) tolerance to phenmedipham were treated, then exposed to half (shaded) and full (nonshaded) sunlight. Nonshaded Nordic treated with phenmedipham had 65% lower dry weight compared to similarly treated plants grown under shade, suggesting that spinach tolerance to phenmedipham was mainly affected by light intensity. Measurements of electron transfer intensity in photosystem II also showed tolerance to phenmedipham that varied among spinach varieties and light intensity. The maximum values of electron transfer in photosystem II of Regal treated with phenmedipham were higher than those of similarly treated Nordic. In the field, phenmedipham was applied under varied light and temperature conditions. The impact of light intensity on yield of treated spinach was greater than the impact of temperature. Phenmedipham applied under high light conditions was more injurious than when applied under low light conditions. Results from this study can contribute to successful integration of phenmedipham into currently used fresh spinach weed management, which in turn can allow more efficient production of this crop. Nomenclature: Phenmedipham; spinach, Spinacia oleracea L. SPQOL; ‘Nordic’; ‘Regal’.

Burning Nettle (Urtica urens) Germination and Seedbank Characteristics in Coastal California

Burning nettle is a noxious weed that commonly infests coastal California vegetable fields. Weed control programs for lettuce and fresh spinach grown in this area do not adequately control burning nettle, and escaped weeds that mature are highly problematic during hand weeding and harvesting. Information on the biology and ecology of burning nettle is limited, and work was conducted to develop information about this weed. The objectives of this study were to evaluate the effect of temperature on burning nettle germination and to determine its base temperature value, to characterize the germination pattern of this weed and seedbanks under local California coastal conditions, and to estimate the optimal timing for burning nettle removal by herbicides and physical methods. The upper optimal temperature for burning nettle germination was 22.8 C, but there was no difference in the final germination percentage between 4 and 22.8 C. The base temperature was determined to be 3 ± 0.2 C, and this information allowed the development of temperature-based optimal control timing models. In the field, burning nettle emerged throughout the year without any seasonal pattern, and germinable seeds were also found in the seedbank throughout the year. Burning nettle was able to complete a growth cycle throughout the year in coastal California. Burning nettle has a short growth cycle that allows it to set viable seeds within 466 ± 13 growing degree days (GDD), and this timing is critical for burning nettle removal by herbicides, cultivation, or hand weeding. The optimal timing for phenmedipham application at 180 g ai ha−1 was estimated to be 205 GDD. The germination and seedbank field studies indicate why burning nettle is so well adapted to the mild climate of coastal California. However, results presented here suggest strategies to reduce the burning nettle seedbank, improve its control, and allow more efficient lettuce and fresh spinach production. Nomenclature: Phenmedipham; burning nettle, Urtica urens L., lettuce, Lactuca sativa L. var., spinach, Spinacia oleracea L. # SPQOL.

Using genetically modified tomato crop plants with purple leaves for absolute weed/crop classification

BACKGROUND Weed/crop classification is considered the main problem in developing precise weed-management methodologies, because both crops and weeds share similar hues. Great effort has been invested in the development of classification models, most based on expensive sensors and complicated algorithms. However, satisfactory results are not consistently obtained due to imaging conditions in the field. RESULTS We report on an innovative approach that combines advances in genetic engineering and robust image-processing methods to detect weeds and distinguish them from crop plants by manipulating the crops leaf color. We demonstrate this on genetically modified tomato (germplasm AN-113) which expresses a purple leaf color. An autonomous weed/crop classification is performed using an invariant-hue transformation that is applied to images acquired by a standard consumer camera (visible wavelength) and handles variations in illumination intensities. CONCLUSION The integration of these methodologies is simple and effective, and classification results were accurate and stable under a wide range of imaging conditions. Using this approach, we simplify the most complicated stage in image-based weed/crop classification models.