Modeling Water and Nutrient Movement in Sandy Soils Using HYDRUS-2D.

Models help to describe and predict complex processes and scenarios that are difficult to understand or measure in environmental management systems. Thus, model simulations were performed (i) to calibrate HYDRUS-2D for water and solute movement as a possible decision support system for Candler and Immokalee fine sand using data from microsprinkler and drip irrigation methods, (ii) to validate the performance of HYDRUS-2D using field data of microsprinkler and drip irrigation methods, and (iii) to investigate Br, NO, and water movement using annual or seasonal weather data and variable fertigation scenarios. The model showed reasonably good agreement between measured and simulated values for soil water content ( = 0.87-1.00), Br ( = 0.63-0.96), NO-N ( = 0.66-0.98), P ( = 0.25-0.78), and K ( = 0.44-0.99) movement. The model could be successfully used for scheduling irrigation and predicting nutrient leaching for both microsprinkler and drip irrigation systems on Florida's sandy soils.

Screen Mesh Size for Exclusion of Diaphorina citri (Hemiptera: Liviidae) in Citrus Production.

Huanglongbing is a citrus disease that reduces yield, crop quality, and eventually causes tree mortality. The putative causal agent, Candidatus Liberibacter asiaticus (Rhizobiales: Rhizobiaceae), is vectored by the Asian citrus psyllid, Diaphorina citri Kuwayama. Disease management is largely through vector control, but the insect is developing pesticide resistance. A nonchemical approach to vector management is to grow citrus under screen cages either as bags over individual trees or enclosures spanning many acres. The enclosing screen reduces wind, alters temperature relative to ambient, and excludes a variety of pests that are too large to pass through the screen. Here we evaluated the potential of six screens to exclude D. citri. We conclude that screens with rectangular openings need to limit the short side to no more than 384.3 µm with a SD of 36.9 µm (40 mesh) to prevent psyllids from passing through the screen. The long side can be at least 833 µm, but the efficacy of screens exceeding this value should be tested before using in the field.

Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network.

Goosegrass is a problematic weed species in Florida vegetable plasticulture production. To reduce costs associated with goosegrass control, a post-emergence precision applicator is under development for use atop the planting beds. To facilitate in situ goosegrass detection and spraying, tiny- You Only Look Once 3 (YOLOv3-tiny) was evaluated as a potential detector. Two annotation techniques were evaluated: (1) annotation of the entire plant (EP) and (2) annotation of partial sections of the leaf blade (LB). For goosegrass detection in strawberry, the F-score was 0.75 and 0.85 for the EP and LB derived networks, respectively. For goosegrass detection in tomato, the F-score was 0.56 and 0.65 for the EP and LB derived networks, respectively. The LB derived networks increased recall at the cost of precision, compared to the EP derived networks. The LB annotation method demonstrated superior results within the context of production and precision spraying, ensuring more targets were sprayed with some over-spraying on false targets. The developed network provides online, real-time, and in situ detection capability for weed management field applications such as precision spraying and autonomous scouts.

Weed Detection in Perennial Ryegrass With Deep Learning Convolutional Neural Network.

Precision herbicide application can substantially reduce herbicide input and weed control cost in turfgrass management systems. Intelligent spot-spraying system predominantly relies on machine vision-based detectors for autonomous weed control. In this work, several deep convolutional neural networks (DCNN) were constructed for detection of dandelion (Taraxacum officinale Web.), ground ivy (Glechoma hederacea L.), and spotted spurge (Euphorbia maculata L.) growing in perennial ryegrass. When the networks were trained using a dataset containing a total of 15,486 negative (images contained perennial ryegrass with no target weeds) and 17,600 positive images (images contained target weeds), VGGNet achieved high F1 scores (≥0.9278), with high recall values (≥0.9952) for detection of E. maculata, G. hederacea, and T. officinale growing in perennial ryegrass. The F1 scores of AlexNet ranged from 0.8437 to 0.9418 and were generally lower than VGGNet at detecting E. maculata, G. hederacea, and T. officinale. GoogleNet is not an effective DCNN at detecting these weed species mainly due to the low precision values. DetectNet is an effective DCNN and achieved high F1 scores (≥0.9843) in the testing datasets for detection of T. officinale growing in perennial ryegrass. Moreover, VGGNet had the highest Matthews correlation coefficient (MCC) values, while GoogleNet had the lowest MCC values. Overall, the approach of training DCNN, particularly VGGNet and DetectNet, presents a clear path toward developing a machine vision-based decision system in smart sprayers for precision weed control in perennial ryegrass.

Detection of broadleaf weeds growing in turfgrass with convolutional neural networks.

BACKGROUND: Weed infestations reduce turfgrass aesthetics and uniformity. Postemergence (POST) herbicides are applied uniformly on turfgrass, hence areas without weeds are also sprayed. Deep learning, particularly the architecture of convolutional neural network (CNN), is a state-of-art approach to recognition of images and objects. In this paper, we report deep learning CNN (DL-CNN) models that are remarkably accurate at detection of broadleaf weeds in turfgrasses. RESULTS: VGGNet was the best model for detection of various broadleaf weeds growing in dormant bermudagrass [Cynodon dactylon (L.)] and DetectNet was the best model for detection of cutleaf evening-primrose (Oenothera laciniata Hill) in bahiagrass (Paspalum notatum Flugge) when the learning rate policy was exponential decay. These models achieved high F1 scores (>0.99) and overall accuracy (>0.99), with recall values of 1.00 in the testing datasets. CONCLUSION: The results of the present research demonstrate the potential for detection of broadleaf weed using DL-CNN models for detection of broadleaf weeds in turfgrass systems. Further research is required to evaluate weed control in field conditions using these models for in situ video input in conjunction with a smart sprayer. © 2019 Society of Chemical Industry.