Search Results (6)

The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in a manner that is both environmentally sustainable and economically advantageous. Weed classification for autonomous agricultural robots is a challenging task for a single-camera-based system due to noise, vibration, and occlusion. To address this issue, we present a multi-camera-based system with decision-level sensor fusion to improve the limitations of a single-camera-based system in this paper. This study involves the utilization of a convolutional neural network (CNN) that was pre-trained on the ImageNet dataset. The CNN subsequently underwent re-training using a limited weed dataset to facilitate the classification of three distinct weed species: Xanthium strumarium (Common Cocklebur), Amaranthus retroflexus (Redroot Pigweed), and Ambrosia trifida (Giant Ragweed). These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. But the accuracy of CNN-based classification from video feed from a single camera was found to deteriorate due to noise, vibration, and partial occlusion of weeds. Test results from a single-camera video feed show that weed classification accuracy is not always accurate for the spray system of an agricultural robot (AgBot). To improve the accuracy of the weed classification system and to overcome the shortcomings of single-sensor-based classification from CNN, an improved Dempster–Shafer (DS)-based decision-level multi-sensor fusion algorithm was developed and implemented. The proposed algorithm offers improvement on the CNN-based weed classification when the weed is partially occluded. This algorithm can also detect if a sensor is faulty within an array of sensors and improves the overall classification accuracy by penalizing the evidence from a faulty sensor. Overall, the proposed fusion algorithm showed robust results in challenging scenarios, overcoming the limitations of a single-sensor-based system. Full article

Understanding the host-selection behavior of herbivorous insects is important to clarify their efficacy and safety as biocontrol agents. To explore the host-plant selection of the beetle Ophraella communa, a natural enemy of the alien invasive common ragweed (Ambrosia artemisiifolia), we conducted a series of outdoor choice experiments in cages in 2010 and in open fields in 2010 and 2011 to determine the preference of O. communa for A. artemisiifolia and three non-target plant species: sunflower (Helianthus annuus), cocklebur (Xanthium sibiricum), and giant ragweed (Ambrosia trifida). In the outdoor cage experiment, no eggs were found on sunflowers, and O. communa adults rapidly moved from sunflowers to the other three plant species. Instead, adults preferred to lay eggs on A. artemisiifolia, followed by X. sibiricum and A. trifida, although very few eggs were observed on A. trifida. Observing the host-plant selection of O. communa in an open sunflower field, we found that O. communa adults always chose A. artemisiifolia for feeding and egg laying. Although several adults (<0.02 adults/plant) stayed on H. annuus, no feeding or oviposition were observed, and adults quickly transferred to A. artemisiifolia. In 2010 and 2011, 3 egg masses (96 eggs) were observed on sunflowers, but they failed to hatch or develop into adults. In addition, some O. communa adults crossed the barrier formed by H. annuus to feed and oviposit on A. artemisiifolia planted in the periphery, and persisted in patches of different densities. Additionally, only 10% of O. communa adults chose to feed and oviposit on the X. sibiricum barrier. These findings suggest that O. communa poses no threat to the biosafety of H. anunuus and A. trifida and exhibits a robust dispersal capacity to find and feed on A. artemisiifolia. However, X. sibiricum has the potential to be an alternative host plant for O. communa. Full article

Accurate information on the spatial distribution of weeds is the key to effective site-specific weed management and the efficient and sustainable use of weed control measures. This work focuses on the early detection of johnsongrass, common cocklebur and velvetleaf present in a corn field using high resolution airborne hyperspectral imagery acquired when corn plants were in a four to six leaf growth stage. Following the appropriate radiometric and geometric corrections, two supervised classification techniques, such as spectral angle mapper (SAM) and spectral mixture analysis (SMA) were applied. Two different procedures were compared for endmember selections: field spectral measurements and automatic methods to identify pure pixels in the image. Maps for both, overall weeds and for each of the three weed species, were obtained with the different classification methods and endmember sources. The best results were achieved by defining the endmembers through spectral information collected with a field spectroradiometer. Overall accuracies ranged between 60% and 80% using SAM for maps that do not differentiate the weed species while it decreased to 52% when the three weed species were individually classified. In this case, the SMA classification technique clearly improved the SAM results. The proposed methodology shows it to be a promising prospect to be applicable to low cost images acquired by the new generation of hyperspectral sensors onboard unmanned aerial vehicles (UAVs). Full article

This study was conducted to investigate the minimum doses of the imazamox active ingredient (ai) that provide satisfactory efficacy (>90%) against fat hen (Chenopodium album L.) and common cocklebur (Xanthium strumarium L.). These two weeds are among the most troublesome weeds of bean fields. The minimum dose studies were carried out separately in the 2–4 and 6–8 true leaf stages of both weeds. The experiments were carried out in pots under greenhouse conditions. The experiments were repeated three times. In the first two experiments, the recommended dose of imazamox (100%) together with 75%, 50% and 25% doses were applied to the weeds during the above-mentioned leaf stages. Some pots were left untreated for control. In the third experiments, 12.50% and 6.25% of the recommended doses were also tested. Plant height and the number of leaves were recorded on the 1st, 3rd, 5th, 7th, 14th, 21st and 28th days following the herbicide application. As a result of the studies, it was determined that nearly half the recommended dose (48.18 g ai/da) provides 90% success in the control of common cocklebur (X. strumarium) when applied at the 2–4 true leaf stages, while a lower dose (36.11 g ai/da) is required for obtaining the same control when applied at the 6–8 true leaf stages. For the fat hen (C. album), only a 17.69 g ai/da application dose was found to provide 90% control at the period of 2–4 true leaves, while 21.21 g ai/da was noted to provide 90% control when applied at the 6–8 true leaf stage. The results suggest that the increase in leaf area reduces the imazamox requirement for the control of X. strumarium. Full article

The objective of this study was to explore the physical properties of maize seeds in competition with weeds. The basic and complex geometric characteristics of seeds from maize plants, competing with Datura stramonium L. (DS) or Xanthium strumarium (XS) at different weed densities, were studied. It was found that the basic and complex geometric characteristics of maize seeds, such as dimension, aspect ratio, equivalent diameter, sphericity, surface area and volume, were significantly affected by weed competition. The increase in weed density from 0 to 8 plants m² resulted in an increase in the angle of repose from 27° to 29°, while increasing weed density from 8 to 16 plants m² caused a diminution of the angle of repose down to 28°. Increasing the density of XS and DS to 16 plants m² caused a reduction in the maximum 1000 seed weight of maize by 40.3% and 37.4%, respectively. These weed side effects must be considered in the design of industrial equipment for seed cleaning, grading and separation. To our knowledge, this is the first study to consider the effects of weed competition on maize traits, which are important in industrial processing such as seed aeration, sifting and drying. Full article

Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m²) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm–18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favored some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches. Full article