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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.

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.

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)

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.

Building a physical barrier around citrus trees or groves can prevent contact between trees and Asian citrus psyllids, the carrier of the pathogen that causes huanglongbing. This new 4-page article is for growers, scientists, and industries that are interested in this approach to protect citrus from huanglongbing. Written by Timothy A. Ebert, Arnold W. Schumann, Laura Waldo, Daniel Stanton, and Philippe Rolshausen, and published by the UF/IFAS Department of Soil and Water Sciences.

Using tricalcium phosphate (TCP) to maintain solution P at low concentrations may be a novel approach for increasing root hair proliferation in HLB-endemic citrus groves, potentially improving nutrient uptake and increasing citrus tree health. It is worth noting that mycorrhizal associations with roots can also enhance their surface area for nutrient uptake, with benefits that are similar to root hairs, and that a similar low-P environment is most conducive for optimal mycorrhizal performance.