UAV-Based Heating Requirement Determination for Frost Management in Apple Orchard

Frost is a natural disaster that can cause catastrophic damages in agriculture, while traditional temperature monitoring in orchards has disadvantages such as being imprecise and laborious, which can lead to inadequate or wasteful frost protection treatments. In this article, we presented a heating requirement assessment methodology for frost protection in an apple orchard utilizing unmanned aerial vehicle (UAV)-based thermal and RGB cameras. A thermal image stitching algorithm using the BRISK feature was developed for creating georeferenced orchard temperature maps, which attained a sub-centimeter map resolution and a stitching speed of 100 thermal images within 30 s. YOLOv4 classifiers for six apple flower bud growth stages in various network sizes were trained based on 5040 RGB images, and the best model achieved a 71.57% mAP for a test dataset consisted of 360 images. A flower bud mapping algorithm was developed to map classifier detection results into dense growth stage maps utilizing RGB image geoinformation. Heating requirement maps were created using artificial flower bud critical temperatures to simulate orchard heating demands during frost events. The results demonstrated the feasibility of the proposed orchard heating requirement determination methodology, which has the potential to be a critical component of an autonomous, precise frost management system in future studies.

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Timing and Concentration of Hydrogen Cyanamide Affect Blueberry Bud Development and Flower Mortality

HortScience ◽

10.21273/hortsci.36.5.922 ◽

2001 ◽

Vol 36 (5) ◽

pp. 922-924 ◽

Cited By ~ 12

Author(s):

J.G. Williamson ◽

B.E. Maust ◽

D.S. NeSmith

Keyword(s):

Growth Stages ◽

Flower Bud ◽

Time Intervals ◽

Vaccinium Ashei ◽

Hydrogen Cyanamide ◽

Bud Development ◽

Mild Winter ◽

Fruit Maturity ◽

Bud Growth ◽

Southern Highbush

The effects of hydrogen cyanamide (H2CN2) sprays on vegetative and reproductive bud growth and development were evaluated for `Climax' rabbiteye (Vaccinium ashei Reade) and `Misty' southern highbush blueberry (V. corymbosum L. hybrid). `Climax' plants were sprayed with 0% or 1% H2CN2 (v/v) at each of several time intervals or flower bud growth stages following either 270 or 600 hours of artificial chilling. `Misty' plants were sprayed with 0%, 1%, or 2% H2CN2 (v/v) immediately after exposure to 0, 150, or 300 hours of artificial chilling. H2CN2 application to `Climax' plants at 3 days after forcing (DAF) and at 10% to 30% stage 3 flower bud development dramatically accelerated leafing, and only minimal flower bud damage was observed at these application times. For `Misty', vegetative budbreak was increased and advanced by both H2CN2 spray concentrations, regardless of pretreatment chilling levels; the number of vegetative budbreaks per plant increased with increased concentration. Timing of anthesis did not appear to be affected by H2CN2, but fruit maturity was hastened. Increased pretreatment chilling also hastened fruit development. This effect on maturity appears to be due primarily to increased and accelerated vegetative budbreak, which probably increased leaf: fruit ratios. Greater flower bud mortality from H2CN2 occurred in nonchilled plants than in those chilled for 150 or 300 hours, especially at 2% H2CN2. These results indicate that H2CN2 has potential value in stimulating vegetative bud development, which potentially hastens maturity in blueberries grown under the mild winter conditions of the Southeast. However, spray concentration and timing of application will be critical to successful use of this compound.

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Infection Efficiency of Venturia inaequalis Ascospores as Affected by Apple Flower Bud Developmental Stage

Plant Disease ◽

10.1094/pdis.1997.81.6.661 ◽

1997 ◽

Vol 81 (6) ◽

pp. 661-663 ◽

Cited By ~ 3

Author(s):

S. Sanogo ◽

D. E. Aylor

Keyword(s):

Developmental Stage ◽

Venturia Inaequalis ◽

Growth Stages ◽

Vegetative Shoot ◽

Flower Buds ◽

Flower Bud ◽

Infection Efficiency ◽

Tight Cluster ◽

Bud Growth

The average infection efficiency of ascospores of Venturia inaequalis deposited on cluster leaves of apple flower buds was 6 to 16%, 3 to 9%, and 0.4 to 0.6% at tight cluster, first pink, and full pink-to-bloom, respectively. No lesions were observed on flower bud cluster leaves at petal fall. However, the leaves on the vegetative shoot emerging from the flower bud were highly susceptible; the average infection efficiency of ascospores on these leaves was 6 to 21%. The infection efficiency was more variable on young cluster and vegetative shoot leaves than on developing and mature cluster leaves. Results from this study indicate that differences in infection efficiency of V. inaequalis ascospores could be identified by apple bud growth stages.

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SEASONAL DYNAMICS OF THE SUNFLOWER STEM WEEVIL, CYLINDROCOPTURUS ADSPERSUS (LECONTE) (COLEOPTERA: CURCULIONIDAE), ON CULTIVATED SUNFLOWER IN THE NORTHERN GREAT PLAINS

The Canadian Entomologist ◽

10.4039/ent1191131-12 ◽

1987 ◽

Vol 119 (12) ◽

pp. 1131-1137 ◽

Cited By ~ 6

Author(s):

Laurence D. Charlet

Keyword(s):

South Dakota ◽

Great Plains ◽

Northern Great Plains ◽

Growth Stages ◽

Regression Equations ◽

Flower Bud ◽

Bud Growth ◽

Production Areas ◽

North And South ◽

Early Flower

AbstractThe sunflower stem weevil, Cylindrocopturus adspersus (LeConte), is a pest of cultivated sunflower in the major production areas of North and South Dakota, Minnesota, and Texas. Adults appeared in sunflower plots between 5 and 25 June in 1980–1985, when plants had 4–14 leaves. Oviposition began soon after adults were present and continued into late August. Peak densities of both eggs and adults occurred in mid-July. Larvae hatched between 6 and 11 July, when plants were in the early flower bud growth stages. Larvae fed in the sunflower stalk and moved to the stalk base or root crown to construct overwintering chambers. The seasonal patterns of the weevil’s life stages in 6 years were similar, but population densities varied. Regression equations were developed to predict larval numbers in stalks from number of adults to aid in making control decisions.

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Adjusting Growth Stage Values to Develop a Linear Scale for Apricot Flower Bud Phenology

HortScience ◽

10.21273/hortsci.33.7.1141 ◽

1998 ◽

Vol 33 (7) ◽

pp. 1141-1144 ◽

Cited By ~ 2

Author(s):

Paul T. Austin ◽

Errol W. Hewett ◽

Dominique Noiton ◽

Julie A. Plummer

Keyword(s):

Growth Stage ◽

Simple Technique ◽

Prunus Armeniaca ◽

Growth Stages ◽

Flower Bud ◽

Continuous Scale ◽

Assessment Period ◽

Bud Phenology ◽

Bud Growth ◽

Stage Class

Integer values used to represent apricot (Prunus armeniaca L.) flower bud growth stages in a phenological scale were adjusted by a simple technique based on cumulative counts of bud observations. Adjusted stage values on a new continuous scale were calculated so that differences between consecutive values were proportional to the frequency with which buds were observed in each growth stage class during the entire assessment period. This meant that adjusted scale values were linearly related to bud development rate at 20 °C. The method was applied to a scale describing flower development from budbreak to petal fall for three cultivars of apricot growing under orchard conditions.

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Frost Protection for Georgia Peach Varieties: Current Practices and Information Needs

HortTechnology ◽

10.21273/horttech03590-16 ◽

2017 ◽

Vol 27 (3) ◽

pp. 344-353 ◽

Cited By ~ 2

Author(s):

Skyler Simnitt ◽

Tatiana Borisova ◽

Dario Chavez ◽

Mercy Olmstead

Keyword(s):

Information Needs ◽

Prunus Persica ◽

Management Strategies ◽

Cost Effective ◽

Planning Horizon ◽

Critical Temperatures ◽

Early Season ◽

Protection Method ◽

Protection Systems ◽

Frost Protection

The study focuses on frost protection for early-season (early-ripening) peach (Prunus persica) varieties, which are an important crop for producers in the southeastern United States. Using in-depth interviews with four major Georgia peach producers, we explore their frost protection management strategies. This information is the first step in developing a comprehensive research agenda to advise cost-effective frost protection methods for peach cultivation. We found that peach producers are concerned about frost impacts on their crops. Although early-season peach varieties are particularly susceptible to frost impacts, producers still dedicate significant acreage to these varieties, aiming to extend the market window, satisfy sales contracts, and meet obligations for hired labor. However, early-season varieties do not result in high profits, so producers prefer to concentrate on frost protection for mid- and late-season varieties. Producers employ a variety of frost protection methods, including passive methods (such as planting sensitive varieties in areas less susceptible to frost and adjusting pruning/thinning schedules) and active methods (such as frost protection irrigation and wind machines). The choice among active frost protection methods is based on factors such as the planning horizon, initial investment needs, frequency of frost events, and the effectiveness of the frost protection method. Problem areas that producers identified included improving the effectiveness of frost protection methods; reducing initial investments required to install frost protection systems; and employing better spatial targeting and configuration of frost protection strategies (to reduce investment costs while maintaining or improving the effectiveness of frost protection). Although the initial investment costs of enhanced protection systems may limit producers from actually adopting such methods, the operating costs of such systems are relatively low and have a limited effect on the decision to employ frost protection during a particular frost event. However, producers use information about critical temperatures for different bud stages, and hence, improving the quality of information regarding frost susceptibility can help producers make better frost protection decisions (and potentially reduce electricity costs and water use for frost protection).

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Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

Remote Sensing ◽

10.3390/rs12223684 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3684

Author(s):

Jie Jiang ◽

Zeyu Zhang ◽

Qiang Cao ◽

Yan Liang ◽

Brian Krienke ◽

...

Keyword(s):

Winter Wheat ◽

Dry Matter ◽

Vegetation Index ◽

Nutrition Status ◽

Growth Stages ◽

Growth Indices ◽

N Accumulation ◽

N Nutrition ◽

Red Edge ◽

Aerial Vehicle

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.

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Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

Frontiers in Plant Science ◽

10.3389/fpls.2018.01362 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 13

Author(s):

Biquan Zhao ◽

Jian Zhang ◽

Chenghai Yang ◽

Guangsheng Zhou ◽

Youchun Ding ◽

...

Keyword(s):

Performance Evaluation ◽

Unmanned Aerial Vehicle ◽

Early Growth ◽

Growth Stages ◽

Aerial Vehicle

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New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

Remote Sensing ◽

10.3390/rs12244170 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4170

Author(s):

Pengfei Chen ◽

Fangyong Wang

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Biomass Estimation ◽

Growth Stages ◽

Spectral Indices ◽

Green Band ◽

Validation Data ◽

Texture Parameters ◽

Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

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