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.
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 F
1
scores (≥0.9278), with high recall values (≥0.9952) for detection of
E. maculata, G. hederacea
, and
T. officinale
growing in perennial ryegrass. The F
1
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 F
1
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.
This new 2-page article provides instructions for using the Diagnosis and Recommendation Integrated System, or DRIS, a web tool designed for analyzing leaf nutrient concentrations of Florida citrus. ...Written by Arnold Schumann and published by the UF/IFAS Department of Soil and Water Sciences.
Since the advent of Huanglongbing HLB ( Candidatus Liberibacter asiaticus) in Florida, several preliminary reports have emerged about the positive effects of mineral nutrition on the performance of ...HLB-affected citrus ( Citrus sp.) trees. HLB-affected trees are known to undergo significant feeder root loss. Therefore, studies have focused on foliar nutrient application instead of soil-applied nutrients speculating that the HLB-affected trees root systems may not be competent in nutrient uptake. Some studies also suggest that HLB-affected trees benefit from micronutrients at higher than the recommended rates; however, the results are often inconclusive and inconsistent. To address this, the goal of the present study was to evaluate the nutrient uptake efficiency and the quantitative and qualitative differences in nutrient uptake of HLB-affected trees. HLB-affected and healthy sweet orange ( Citrus sinensis ) trees were grown in a 100% hydroponic system with Hoagland solution for 8 weeks. The trees were deprived of any fertilization for 6 months before the transfer of trees to the hydroponic solution. Altogether, the four treatments studied in the hydroponic system were healthy trees fertilized (HLY-F) and not fertilized (HLY-NF), and HLB-affected trees fertilized (HLB-F) and not fertilized (HLB-NF). HLY-F and HLY-NF trees were found to have similar levels of leaf nutrients except for N, which was found to be low in nonfertilized trees (HLY and HLB). Both HLB-F and HLB-NF trees had lower levels of Ca, Mg, and S compared with HLY trees. In addition, HLB-NF trees had significantly lower levels of micronutrients Mn, Zn, and Fe, compared with HLY-NF trees. The hydroponic solution analysis showed that HLB-F and HLY-F trees had similar uptake of all the nutrients. Considering that HLB-affected trees have a lower root-to-shoot ratio than healthy trees, nutrient uptake efficiency per kilogram of root tissue was significantly higher in HLB trees compared with HLY trees. Under nutrient-deficient conditions (day 0) only nine genes were differentially expressed in HLB roots compared with HLY roots. On the other hand, when fertilizer was supplied for ≈1 week, ≈2300 genes were differentially expressed in HLB-F roots compared with HLY-F roots. A large number of differentially expressed genes in HLB-F were related to ion transport, root growth and development, anatomic changes, cell death, and apoptosis compared with HLY-F trees. Overall, anatomic and transcriptomic analyses revealed that HLB-affected roots undergo remarkable changes on transitioning from no nutrients to a nutrient solution, possibly facilitating a high uptake of nutrients. Our results suggest the roots of HLB-affected trees are highly efficient in nutrient uptake; however, a small root mass is a major limitation in nutrient uptake. Certain micronutrients and secondary macronutrients are also metabolized (possibly involved in tree defense or oxidative stress response) at a higher rate in HLB-affected trees than healthy trees. Therefore, a constant supply of fertilizer at a slightly higher rate than what is recommended for micronutrients and secondary macronutrients would be beneficial for managing HLB-affected trees.
Weed interference during crop establishment is a serious concern for Florida strawberry Fragaria × ananassa (Weston) Duchesne ex Rozier (pro sp.) chiloensis × virginiana producers. In situ remote ...detection for precision herbicide application reduces both the risk of crop injury and herbicide inputs. Carolina geranium (Geranium carolinianum L.) is a widespread broadleaf weed within Florida strawberry production with sensitivity to clopyralid, the only available POST broadleaf herbicide. Geranium carolinianum leaf structure is distinct from that of the strawberry plant, which makes it an ideal candidate for pattern recognition in digital images via convolutional neural networks (CNNs). The study objective was to assess the precision of three CNNs in detecting G. carolinianum. Images of G. carolinianum growing in competition with strawberry were gathered at four sites in Hillsborough County, FL. Three CNNs were compared, including object detection–based DetectNet, image classification–based VGGNet, and GoogLeNet. Two DetectNet networks were trained to detect either leaves or canopies of G. carolinianum. Image classification using GoogLeNet and VGGNet was largely unsuccessful during validation with whole images (Fscore < 0.02). CNN training using cropped images increased G. carolinianum detection during validation for VGGNet (Fscore = 0.77) and GoogLeNet (Fscore = 0.62). The G. carolinianum leaf–trained DetectNet achieved the highest Fscore (0.94) for plant detection during validation. Leaf-based detection led to more consistent detection of G. carolinianum within the strawberry canopy and reduced recall-related errors encountered in canopy-based training. The smaller target of leaf-based DetectNet did increase false positives, but such errors can be overcome with additional training images for network desensitization training. DetectNet was the most viable CNN tested for image-based remote sensing of G. carolinianum in competition with strawberry. Future research will identify the optimal approach for in situ detection and integrate the detection technology with a precision sprayer.
Weed control between plastic covered, raised beds in Florida vegetable crops relies predominantly on herbicides. Broadcast applications of post-emergence herbicides are unnecessary due to the general ...patchy distribution of weed populations. Development of precision herbicide sprayers to apply herbicides where weeds occur would result in input reductions. The objective of the study was to test a state-of-the-art object detection convolutional neural network, You Only Look Once 3 (YOLOV3), to detect vegetation both indiscriminately (1-class network) and to detect and discriminate three classes of vegetation commonly found within Florida vegetable plasticulture row-middles (3-class network). Vegetation was discriminated into three categories: broadleaves, sedges and grasses. The 3-class network (
Fscore
= 0.95) outperformed the 1-class network (
Fscore
= 0.93) in overall vegetation detection. The increase in target variability when combining classes increased and potentially negated benefits from pooling classes into a single target (and increasing the available data per class). The 3-class network
Fscores
for grasses, sedges and broadleaves were 0.96, 0.96 and 0.93 respectively.
Recall
was the limiting factor for all classes. With consideration to how much of the plant was identified (broadleaves and grasses), the 3-class network (
Fscore
= 0.93) outperformed the 1-class network (
Fscore
= 0.79). The 1-class network struggled to detect grassy weed species (
recall
= 0.59). Use of YOLOV3 as an object detector for discrimination of vegetation classes is a feasible option for incorporation into precision applicators.
•Deep convolutional neural networks are highly suitable for weed detection in turfgrass.•A single neural network can detect multiple weed species with different weed densities.•VGGNet out-performed ...GoogLeNet for weed detection in actively growing bermudagrass.•DetectNet effectively detected various weed species in dormant bermudagrass.
Precision spraying of herbicides can significantly reduce herbicide use. The detection system is the critical component within smart sprayers that is used to detect target weeds and make spraying decisions. In this work, we report several deep convolutional neural network (DCNN) models that are exceptionally accurate at detecting weeds in bermudagrass Cynodon dactylon (L.) Pers.. VGGNet achieved high F1 score values (>0.95) and out-performed GoogLeNet for detection of dollar weed (Hydrocotyle spp.), old world diamond-flower (Hedyotis cormybosa L. Lam.), and Florida pusley (Richardia scabra L.) in actively growing bermudagrass. A single VGGNet model reliably detected these summer annual broadleaf weeds in bermudagrass across different mowing heights and surface conditions. DetectNet was the most successful DCNN architecture for detection of annual bluegrass (Poa annua L.) or Poa annua growing with various broadleaf weeds in dormant bermudagrass. DetectNet exhibited an excellent performance for detection of weeds while growing in dormant bermudagrass, with F1 scores >0.99. Based on the high level of performance, we conclude that DCNN-based weed recognition can be an effective decision system in the machine vision subsystem of a precision herbicide applicator for weed control in bermudagrass turfgrasses.
The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate ...application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) for VA application of agrochemicals. Real-time testing of the SVRS took place for detecting and spraying and/or skipping lambsquarters weed and early blight infected and healthy potato plants. About 24,000 images were collected from potato fields in Prince Edward Island and New Brunswick under varying sunny, cloudy, and partly cloudy conditions and processed/trained using YOLOv3 and tiny-YOLOv3 models. Due to faster performance, the tiny-YOLOv3 was chosen to deploy in SVRS. A laboratory experiment was designed under factorial arrangements, where the two spraying techniques (UA and VA) and the three weather conditions (cloudy, partly cloudy, and sunny) were the two independent variables with spray volume consumption as a response variable. The experimental treatments had six repetitions in a 2 × 3 factorial design. Results of the two-way ANOVA showed a significant effect of spraying application techniques on volume consumption of spraying liquid (p-value < 0.05). There was no significant effect of weather conditions and interactions between the two independent variables on volume consumption during weeds and simulated diseased plant detection experiments (p-value > 0.05). The SVRS was able to save 42 and 43% spraying liquid during weeds and simulated diseased plant detection experiments, respectively. Water sensitive papers’ analysis showed the applicability of SVRS for VA with >40% savings of spraying liquid by SVRS when compared with UA. Field applications of this technique would reduce the crop input costs and the environmental risks in conditions (weed and disease) like experimental testing.
Spot spraying POST herbicides is an effective approach to reduce herbicide input and weed control cost. Machine vision detection of grass or grass-like weeds in turfgrass systems is a challenging ...task due to the similarity in plant morphology. In this work, we explored the feasibility of using image classification with deep convolutional neural networks (DCNN), including AlexNet, GoogLeNet, and VGGNet, for detection of crabgrass species (Digitaria spp.), doveweed Murdannia nudiflora (L.) Brenan, dallisgrass (Paspalum dilatatum Poir.), and tropical signalgrass Urochloa distachya (L.) T.Q. Nguyen in bermudagrass Cynodon dactylon (L.) Pers.. VGGNet generally outperformed AlexNet and GoogLeNet in detecting selected grassy weeds. For detection of P. dilatatum, VGGNet achieved high F1 scores (≥0.97) and recall values (≥0.99). A single VGGNet model exhibited high F1 scores (≥0.93) and recall values (1.00) that reliably detected Digitaria spp., M. nudiflora, P. dilatatum, and U. distachya. Low weed density reduced the recall values of AlexNet at detecting all weed species and GoogLeNet at detecting Digitaria spp. In comparison, VGGNet achieved excellent performances (overall accuracy = 1.00) at detecting all weed species in both high and low weed-density scenarios. These results demonstrate the feasibility of using DCNN for detection of grass or grass-like weeds in turfgrass systems.
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