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Fruit external color is one of the primary factors associated with consumer choice at the market level. Grapefruit, pomelos, and grapefruit-like hybrids are fruits that can develop different external colors based on horticultural practices. The fruit of ‘914’, which is a grapefruit-like hybrid, is similar to grapefruit in flavor and appearance; however, it has a higher total soluble solids content. The fruit rind tends to be green-yellow to yellow with a pink blush, depending on the harvest date and site conditions. Fruit bagging is a horticultural practice that has been used for more than a century to improve the product quality and appearance of various fruit crop species. During our experiments, fruits of ‘914’ were bagged at two locations in Central Florida to determine the effects of bagging on external fruit color and internal fruit quality. Both locations had fruit-bearing trees in citrus under protected screen structures to exclude Asian citrus psyllid from the cropping system. Fruits were bagged in Jul 2023 and followed until harvest. At the Alturas, FL, USA site, there was one harvest date; however, at the Citrus Research and Education Center site, there were two harvest dates. Data trees were strip-harvested on each harvest date. Fruit weight, diameter, length, and peel color as well as total soluble solids, titratable acidity, and maturity index (soluble solids:acid) were measured. The results indicated that bagging ‘914’ positively influences external peel color and can affect internal fruit quality. More research is needed to determine the optimal windows of fruit bagging of ‘914’ grapefruit-like hybrid and the effects of bagging on consumer sensory perception and postharvest quality.

The term plant growth-promoting rhizobacteria (PGPR) refers to a root-associated bacteria that possesses several benefits for soil and the plant. The increasing demand for sustainable food production necessitates a shift towards agricultural practices that mitigate adverse environmental impacts. Excessive use of chemical fertilizers and pesticides has raised concerns, prompting a surge of interest in harnessing the potential of beneficial soil microorganisms, particularly plant growth-promoting rhizobacteria. This study explores the impact of soil inoculation with PGPR treatments on grapefruit seedlings, including a commercial strain of Bacillus velezensis, a fresh inoculum of Bacillus amyloliquefaciens, a mixture of three selected citrus endophytic Bacillus spp. isolates, inorganic fertilizers, and combinations thereof. The aim was to test their ability to enhance growth and nutrient uptake reducing the input of chemical fertilization. Results indicated that the combination of Bacillus velezensis and inorganic fertilization significantly improved soil nutrient availability and enhanced plant growth, surpassing both negative (water) and positive (inorganic fertilization) controls. Shoot and root system biomass showed significant increases from Bacillus velezensis plus inorganic fertilization compared to other treatments. These findings provide insights into management methods that can reduce chemical inputs while promoting plant productivity. The demonstrated benefits of PGPR on grapefruit seedlings highlight a promising approach for future research and applications in sustainable citrus cultivation.

Improving fruit quality is important to wild blueberry producers. Plant debris like leaves and stems caused a reduction in fruit quality during mechanical harvesting. A debris detection system was developed using two Logitech C920 webcam cameras mounted before and after the blower fan on a commercial mechanical wild blueberry harvester. Images were collected from two commercially managed wild blueberry fields located in central Nova Scotia to develop a dataset of 1000 images. Two convolutional neural networks (CNNs), YOLOv3 and YOLOv3-SPP, were used and compared for performance analysis. The CNNs were trained using no augmented images, images with five augmentations (sharpening, brightness, contrast, gamma correction, and saturation), and images with five augmentations that were more heavily weighted towards gamma. Four different computer hardware packages (Hewlett-Packard, Shuttle XPC, Jetson TX2, and GPU-based Desktop) were used to determine the appropriate hardware and CNN model combination for real-time performance. YOLOv3 and YOLOv3-SPP were trained to mAP scores of 72.87 and 74.38% using the dataset with more gamma-augmented images. This was an improvement over the dataset with no augmentation (67.75; 68.49%) and equally numbered augmentations (71.26%; 73.03%). YOLOv3-SPP combined with an Intel® Core™ i9-7900X CPU and Nvidia GeForce RTX™ 2080 Ti GPU in a desktop computer achieved the fastest detection rate of 33.30 ms. The detection speed on all other hardware configurations exceeded 33.30 ms, indicating that a powerful desktop GPU is required for real-time performance of this task. With adequate processing hardware, the developed technology could be integrated into a control system to automatically adjust brushes based on conveyor information from mechanical wild blueberry harvesters to make cleaning berries easier and enhance fruit quality.

The field performance of a newly developed novel smart variable-rate sprayer was evaluated. The sprayer uses convolutional neural networks (CNNs) for target detection and spot-applications of agrochemicals within potato (Solanum tuberosum L.) fields attacked by lamb's quarters (Chenopodium album L.) and corn spurry (Spergula arvensis L.) weeds and the early blight potato disease caused by Alternaria solani Sorauer. There was a non-significant effect of treatment conditions (i.e., cloudy, partly cloudy, and sunny) on spray volume during weed and diseased plant detection experiments (p-value = 0.93 and 0.75, respectively) showing that the smart sprayer performed well during all treatment conditions. There was a significant effect of spraying application techniques on the use of spray volume (p-value ≤ 0.05) reflecting a significant saving of spraying liquid during variable-rate application (VA). On average, the sprayer reduced spray volume by 47 and 51% for weeds and diseased plant detection experiments as compared to the values of chemicals applied at constant-rate application (CA), respectively, under all treatment conditions. The analysis of water-sensitive papers (WSP) data resulted in non-significant differences between CA and VA under all field conditions. These results suggest that this sprayer has a great potential to get a suitable spot application of agrochemicals and reduce the use of plant protection products thereby ensuring farm profits and environmental stewardship.

This study looked at the development of six deep learning artificial neural network models for detecting ripeness stage in wild blueberries, along with developing models for yield estimation. The six networks used were YOLOv3, YOLOv3-SPP, YOLOv3-Tiny, YOLOv4, YOLOv4-Small and YOLOv4-Tiny. Both 3-class (green berries, red berries, blue berries) and 2-class (unripe berries, ripe berries) models were developed with YOLOv4 performing the best with mean average precisions of 79.79% and 88.12% respectively. This result was further supported by YOLOv4 achieving the highest F1 score of 0.82. YOLOv4-Tiny performed the best from a computational load perspective having a mean inference time of 7.8 ms and a mean memory usage of 1.63 GB for single 1280 × 736 pixel images. Only minor differences in the accuracy of the nonlinear regression yield prediction models were detected, with YOLOv4-Small performing the best with a mean absolute error of 24.1%. Despite this error, the results are encouraging, and this novel approach to yield estimation in wild blueberries will aid growers in making better, more localized, management decisions, improving yields and ultimately increasing profits by better understanding their fields ripening characteristics.

Agricultural herbicide application efficiency can be improved using smart sprayers which provide site-specific, rather than broadcast, applications of agrochemicals. The YOLOv3-Tiny convolutional neural network (CNN) was trained to detect two weeds, hair fescue and sheep sorrel, in images captured from wild blueberry fields throughout Nova Scotia, Canada. An evaluation was performed in three commercial wild blueberry fields in Nova Scotia to examine the effects of camera selection and target distance on detection accuracy. A Canon T6 DSLR camera, an LG G6 smartphone, and a Logitech c920 webcam were used to capture RGB images at varying distances from target weeds. Mean F1-scores for each combination of camera and image height were analysed in a 3 × 3 factorial arrangement for hair fescue and a 3 × 2 factorial arrangement for sheep sorrel. Images captured from 0.98 m with the LG G6 and Canon T6 produced F1-scores of up to 0.97 for detection of at least one hair fescue tuft. Images captured with the LG G6 and Canon T6 DSLR from 0.57 m achieved F1-scores of 0.94 and 0.93, respectively, for detection of at least one sheep sorrel plant per image. Sheep sorrel was undetectable in images from the Logitech c920 under 19 of 27 parameter combinations. Future work will involve using the CNN to control herbicide applications with a real-time smart sprayer. Additionally, the CNN will be used in a web-based application to detect target weeds and provide site-specific information to aid management decisions. Using a CNN to detect weeds will create improvements in management techniques, resulting in cost-savings and greater sustainability for the wild blueberry industry.

A Gabor wavelets based technique was investigated as a potential tool for scene classification (into one of bare patch, plant, or weed) for its ultimate utility in site-specific application of agrochemicals in wild blueberry fields.Images were gathered from five sites located in central Nova Scotia, Canada. Gabor wavelet features extracted from these images were used to classify scenes according to visually determined classes using step-wise linear discriminant analysis.For individual fields, classification accuracy attained ranged between 87.9% and 98.3%; selected Gabor features ranged between 27 and 72; contextual accuracy for herbicide ranged between 67.5% and 96.7%, and contextual accuracy for fertilizer ranged between 63.6% and 97.1%. The pooled scenes yielded a classification accuracy of 81.4%, and contextual accuracy figures of 61.1% and 73.1% for herbicide and fertilizer, respectively, with selected Gabor features of 36.Calibrations based on LDA coefficients from the pooled scenes could help avoid the need to re-calibrate for each field, whereas those based on individual field LDA coefficients could improve accuracy, hence enable saving on expensive agrochemicals.

Abstract 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.

The conventional gravimetric methods of estimating soil moisture content (θ) are laborious, time-consuming, and destructive to agricultural fields. We evaluated the performance of DualEM-II sensor in non-destructive way of θ prediction and for predicting θ variations within potato fields in Atlantic Canada. Values of θ were measured from four potato fields in New Brunswick and Prince Edward Island using a pre-calibrated (R2 = 0.98) time domain reflectometry (TDR) from root zone of potato tubers under grid sampling arrangements. Horizontal co-planar (HCP) and perpendicular co-planar (PRP) readings were taken using DualEM-II sensor from the same locations of θ measurements. There was a better correlation between PRP and θ (r: 0.64-0.83) was calculated than between HCP and θ (r: 0.41-0.79). There was no significant difference (R2: 0.60-0.69; RMSE (root mean square error): 2.32-4.02) between the θ values measured with TDR (θM) and those predicted with DualEM-II (θP) confirming that the use of electromagnetic induction technique, evaluated during this study, is labor saving, quick, non-destructive, and accurate and can be considered a precision agriculture tool for efficiently managing soil water in potato fields.

The information provided in the 2008 2nd edition is still sound for healthy citrus trees under Florida production conditions. Much of the information provided in this document on nutrients, application methods, leaf and soil sampling and irrigation scheduling are also effective for huanglongbing (HLB) affected citrus trees. However, research conducted since HLB was detected in Florida in 2005 has established changes in many production practices, including nutrient rates, irrigation scheduling, soil pH management, and use of Citrus Under Protective Screen (CUPS). Changes to the 2nd edition of SL253 will appear in boxes similar to this one at the beginnings of chapters 2, 6, 8, 9, and 11.

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.

This 2-page fact sheet is part of the 2019–2020 Florida Citrus Production Guide. Written by A. W. Schumann, A. Singerman, A. L. Wright, and R. S. Ferrarezi, and published by the Horticultural Sciences Department, March 2019. CMG19/HS1304: 2022–2023 Florida Citrus Production Guide: Citrus under Protective Screen (CUPS) Production Systems (ufl.edu)

This 5-page fact sheet is part of the 2019–2020 Florida Citrus Production Guide. This chapter on irrigation management of citrus is largely taken from guidelines provided in SL253 Chapter 9 on trees prior to citrus greening: SL253/SS478: Nutrition of Florida Citrus Trees, 3rd Edition. A section has been added to cover recent findings on water use of trees affected by citrus greening and the impact this would have on the irrigation management considerations. Written by D. M. Kadyampakeni, K. T. Morgan, M. Zekri, R. S. Ferrarezi, A. W. Schumann, and T. A. Obreza, and published by the Agronomy Department, March 2019. CPG12/CG093: 2022–2023 Florida Citrus Production Guide: Irrigation Management of Citrus Trees (ufl.edu)

Fertilization is an important aspect of growing citrus commercially. Citrus tree growth, health, and fruit production and quality are closely affected by plant nutrition. This 3-page fact sheet is part of the 2019–2020 Florida Citrus Production Guide. Written by M. Zekri, A. W. Schumann, T. Vashisth, D. M. Kadyampakeni, K. T. Morgan, B. Boman, and T. Obreza, and published by the Agronomy Department, March 2019. CMG14/CG092: 2022–2023 Florida Citrus Production Guide: Fertilizer Application Methods (ufl.edu)

The following description of citrus fertilizer uptake, soil and leaf testing and nutrient recommendations were taken from SL 253* and reflects citrus nutrient management for healthy trees prior to citrus greening or HLB entering Florida. Additional information on nutrients obtained since the publication was released appear in this document as "the effect of HLB" on various aspects of citrus nutrient management and are noted as such with published papers cited. *Nutrition of Florida Citrus Trees, 2nd Edition By Thomas A. Obreza and Kelly T. Morgan (2008) SL253/SS478: Nutrition of Florida Citrus Trees, 3rd Edition. (ufl.edu) This 9-page fact sheet is part of the 2019–2020 Florida Citrus Production Guide. Written by K. T. Morgan, D. M. Kadyampakeni, M. Zekri, A. W. Shumann, T. Vashisth, and T. A. Obreza, and published by the Agronomy Department, March 2019. CMG13/CG091: 2022–2023 Florida Citrus Production Guide: Nutrition Management for Citrus Trees (ufl.edu)

Deep learning convolutional neural networks (CNNs) are an emerging technology that provide an opportunity to increase agricultural efficiency through remote sensing and automatic inferencing of field conditions. This paper examined the novel use of CNNs to identify two weeds, hair fescue and sheep sorrel, in images of wild blueberry fields. Commercial herbicide sprayers provide a uniform application of agrochemicals to manage patches of these weeds. Three object-detection and three image-classification CNNs were trained to identify hair fescue and sheep sorrel using images from 58 wild blueberry fields. The CNNs were trained using 1280x720 images and were tested at four different internal resolutions. The CNNs were retrained with progressively smaller training datasets ranging from 3780 to 472 images to determine the effect of dataset size on accuracy. YOLOv3-Tiny was the best object-detection CNN, detecting at least one target weed per image with F1-scores of 0.97 for hair fescue and 0.90 for sheep sorrel at 1280 × 736 resolution. Darknet Reference was the most accurate image-classification CNN, classifying images containing hair fescue and sheep sorrel with F1-scores of 0.96 and 0.95, respectively at 1280 × 736. MobileNetV2 achieved comparable results at the lowest resolution, 864 × 480, with F1-scores of 0.95 for both weeds. Training dataset size had minimal effect on accuracy for all CNNs except Darknet Reference. This technology can be used in a smart sprayer to control target specific spray applications, reducing herbicide use. Future work will involve testing the CNNs for use on a smart sprayer and the development of an application to provide growers with field-specific information. Using CNNs to improve agricultural efficiency will create major cost-savings for wild blueberry producers.

This study evaluated the potential of using machine vision in combination with deep learning (DL) to identify the early blight disease in real-time for potato production systems. Four fields were selected to collect images (n = 5199) of healthy and diseased potato plants under variable lights and shadow effects. A database was constructed using DL to identify the disease infestation at different stages throughout the growing season. Three convolutional neural networks (CNNs), namely GoogleNet, VGGNet, and EfficientNet, were trained using the PyTorch framework. The disease images were classified into three classes (2-class, 4-class, and 6-class) for accurate disease identification at different growth stages. Results of 2-class CNNs for disease identification revealed the significantly better performance of EfficientNet and VGGNet when compared with the GoogleNet (FScore range: 0.84–0.98). Results of 4-Class CNNs indicated better performance of EfficientNet when compared with other CNNs (FScore range: 0.79–0.94). Results of 6-class CNNs showed similar results as 4-class, with EfficientNet performing the best. GoogleNet, VGGNet, and EfficientNet inference time values ranged from 6.8–8.3, 2.1–2.5, 5.95–6.53 frames per second, respectively, on a Dell Latitude 5580 using graphical processing unit (GPU) mode. Overall, the CNNs and DL frameworks used in this study accurately classified the early blight disease at different stages. Site-specific application of fungicides by accurately identifying the early blight infected plants has a strong potential to reduce agrochemicals use, improve the profitability of potato growers, and lower environmental risks (runoff of fungicides to water bodies).

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.

Water is a limiting factor in Florida citrus production during the majority of the year because of the low water holding capacity of sandy soils resulting from low clay and the non-uniform distribution of the rainfall. In Florida, the major portion of rainfall comes in June through September. However, rainfall is scarce during the dry period from February through May, which coincides with the critical stages of bloom, leaf expansion, fruit set, and fruit enlargement. Irrigation is practiced to provide water when rainfall is not sufficient or timely to meet water needs. Proper irrigation scheduling is the application of water to crops only when needed and only in the amounts needed; that is, determining when to irrigate and how much water to apply. With proper irrigation scheduling, yield will not be limited by water stress. With citrus greening (HLB), irrigation scheduling is becoming more important and critical and growers cannot afford water stress or water excess. Any degree of water stress or imbalance can produce a deleterious change in physiological activity of growth and production of citrus trees. The number of fruit, fruit size, and tree canopy are reduced and premature fruit drop is increased with water stress. Extension growth in shoots and roots and leaf expansion are all negatively impacted by water stress. Other benefits of proper irrigation scheduling include reduced loss of nutrients from leaching as a result of excess water applications and reduced pollution of groundwater or surface waters from the leaching of nutrients. Recent studies have shown that for HLB-affected trees, irrigation frequency should increase and irrigation amounts should decrease to minimize water stress from drought stress or water excess, while ensuring optimal water availability in the rootzone at all times.

Non-uniform tree growth and fruit yield are very common throughout many Florida citrus groves, but variable groves are typically managed as if they were uniform. This 4-page fact sheet provides information about the relationship between soil variability and citrus production, proposes recommendations for soil sampling that account for spatial variability, and suggests site-specific management practices for variable Florida citrus groves. Written by Kirandeep K. Mann, Arnold W. Schumann, Thomas A. Obreza, Willie G. Harris, and Jerry B. Sartain, and published by the UF Department of Soil and Water Science, January 2012. SL556/SS557: How to Characterize Soil Variability in Florida Citrus Groves as It Relates to Tree Growth and Yield (ufl.edu)

HS1181, a 5-page illustrated fact sheet by Timothy M. Spann and Arnold W. Schumann, provides a summary of plant mineral nutrition, discussing how nutrient levels may affect plant growth and potential susceptibility to plant diseases and pests. Published by the UF Department of Horticultural Sciences, July 2010.

HS1165, a 7-page fact sheet by Timothy M. Spann, Ryan A. Atwood, Megan M. Dewdney, Robert C. Ebel, Reza Ehsani, Gary England, Stephen H. Futch, Tim Gaver, Tim Hurner, Chris Oswalt, Michael E. Rogers, Fritz M. Roka, Mark A. Ritenour, Mongi Zekri, Brian J. Boman, Kuang-Ren Chung, Michelle D. Danyluk, Renee Goodrich-Schneider, Kelly T. Morgan, Robert A. Morris, Ronald P. Muraro, Pamela Roberts, Robert E. Rouse, Arnold W. Schumann, Philip A. Stansly, and Lukasz L. Stelinski, provides guidance to the Florida citrus industry in making management decisions regarding huanglongbing (HLB, citrus greening). Published by the UF Department of Horticultural Sciences, June 2010.