Real-time nondestructive citrus fruit quality monitoring system: development and laboratory testing

This study reports on the development and laboratory testing of the This study reports on the development and laboratory testing of the nondestructive citrus fruit quality monitoring system.  Prototype system consists of a light detection and ranging (LIDAR) and visible-near infrared spectroscopy sensors installed on an inclined conveyer for real-time fruit size and total soluble solids (TSS) measurement respectively.  Laboratory test results revealed that the developed system is applicable for instantaneous fruit size (R2 = 0.912) and TSS (R2 = 0.677, standard error of prediction = 0.48 °Brix) determination.  Future applications of such system would be in precision farming for in-field orange quality determination during the harvest and for row specific yield mapping and monitoring.    Keywords: LIDAR sensor, visible-near infrared spectroscopy, fruit size, sugar conten

Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology

The pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore, this study evaluates the role of ground-based multispectral imagery derived vegetation indices (VIs) for irrigated the pinto bean stress and yield assessments. Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52% and 100% of required evapotranspiration. Imagery data was acquired using a five-band multispectral imager at early, mid and late growth stages. Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential. Principal component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation (rs) with abiotic stress at each growth stage. Transformed difference vegetation index, nonlinear vegetation index (NLI), modified NLI and infrared percentage vegetation index (IPVI) were consistent in accounting the stress response and crop yield at all growth stages (rs \u3e 0.60, coefficient of determination (R2): 0.50–0.56, P \u3c 0.05). Ten other VIs significantly accounted for crop stress at early and late stages. Overall, identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments

Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Global plant genetics research efforts have focused on developing high yielding, stress tolerant, and disease resistant row and field crop varieties that are more efficient in their use of agronomic inputs (water, nutrients, pesticides, etc.). Until recently, a key bottleneck in such research was the lack of high-throughput sensing technologies for effective and rapid evaluation of expressed phenotypes under field conditions for holistic data-driven decision making and variety selection. This review focuses on technological aspects of integrating unmanned aerial vehicles with imaging systems to enhance field phenotyping capabilities. The state-of-the-art of unmanned aerial vehicle technology for various applications including crop emergence, vigor, and characterization of yield potential of row and field crops has been reviewed. The potential of using aerial imaging to evaluate resistance/susceptibility to biotic and abiotic stress for crop breeding and precision production management has been discussed along with future perspectives and developments.Keywords: High-throughput field phenomics, Aerial imaging, Crop breeding, Data minin

Air-assisted velocity profiles and perceptive canopy interactions of commercial airblast sprayers used in Pacific Northwest perennial specialty crop production

This study aimed at providing a data-driven understanding of air velocity profiles for four commercial airblast sprayers widely used in the Pacific Northwest (PNW) region of the United States. The Rear’s Powerblast (S1) and Pakblast (S2), Turbomist 30P (S3), and Columbia Accutec (S4) were evaluated using the Smart Spray Analytical System (Bahlol et al., 2020). Air velocity contour profiles and geometrical attributes (symmetry and uniformity) were characterized on two sides as well as at three horizontal distances from the outlet (0.6, 1.5, and 2.1 m) of each sprayer. The air velocity profiles were matched to vertical canopy zones of three typical perennial specialty crops (cherry, apple, and grapevine) to identify their suitability. Air velocity differences between the two sides were majorly significant (p<0.05) for selected sprayers with magnitudes higher on the right side for S1 and S4, and on the left side for S3, but insignificant (p>0.05) for S2. Air delivery pattern symmetry was very high for sprayers S1 (95±3%, mean ± standard deviation), S2 (82±6%), and S3 (83±3%) compared to S4 (64±14%), while uniformity was high for all sprayers (left: 51–72%, right: 59–73%). Profiles suggest that the current configuration of sprayer S2 would suit for applications on modified vertical shoot position grapevine or comparable short tree fruit canopies. On the other hand, sprayers S1, S3, and S4 would be better suited for spray applications in fruiting zones of taller canopies such as central leader apple and steep leader trained cherry crop. Overall, such air delivery evaluations would aid to determine required sprayer adjustments for efficient agrochemical applications on various perennial specialty crops and canopy architectures

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples

Bitter pit is one of the most important disorders in apples. Some of the fresh market apple varieties are susceptible to bitter pit disorder. In this study, visible–near-infrared spectrometry-based reflectance spectral data (350–2500 nm) were acquired from 2014, 2015 and 2016 harvest produce after 63 days of storage at 5 °C. Selected spectral features from 2014 season were used to classify the healthy and bitter pit samples from three years. In addition, these spectral features were also validated using hyperspectral imagery data collected on 2016 harvest produce after storage in a commercial storage facility for 5 months. The hyperspectral images were captured from either sides of apples in the range of 550–1700 nm. These images were analyzed to extract additional set of spectral features that were effective in bitter pit detection. Based on these features, an automated spatial data analysis algorithm was developed to detect bitter pit points. The pit area was extracted, and logistic regression was used to define the categorizing threshold. This method was able to classify the healthy and bitter pit apples with an accuracy of 85%. Finally, hyperspectral imagery derived spectral features were re-evaluated on the visible–near-infrared reflectance data acquired with spectrometer. The pertinent partial least square regression classification accuracies were in the range of 90–100%. Overall, the study identified salient spectral features based on both hyperspectral spectrometry and imaging techniques that can be used to develop a sensing solution to sort the fruit on the packaging lines

Assessing Suitability of Auto-Selection of Hot and Cold Anchor Pixels of the UAS-METRIC Model for Developing Crop Water Use Maps

The METRIC energy balance model uses an auto-selection approach for identifying hot (dry, bare soil) and cold (fully transpiring crop) anchor pixels for the internal calibration of the model. When an unmanned aerial system (UAS) is used for imagery, the small image size and the varying crop and soil water status of agricultural fields make the identification of reliable hot and cold pixels challenging. In this study, we used an experimental spearmint field under three irrigation levels (75%, 100%, and 125% of crop evapotranspiration, ETc). As a way of providing diverse field conditions, six different extents (Extent 1 to Extent 6) were selected from each day of the seven days of UAS imagery campaigns of the same field for generating UAS-based ETc maps using auto-selection of hot and cold anchor pixels for the internal calibration of the model. Extent 1 had the smallest coverage area of the field, including only plants that were irrigated at 75% of ETc, while the fields of view of the other extents increased to where the Extent 6 covered the spearmint field and all the surroundings including trees, a nearby water canal, irrigated grass, and irrigated and non-irrigated soil. The results showed that different sizes of extent resulted in the selection of variable hot (bare, but moist soil in small extents, and dry bare soil at the larger extents) and cold anchor pixels (crop under water stress at the small extents, and tree canopy or grass alongside the water canal at the larger extents). This variation resulted in significantly different ETc estimation for the same spearmint crop field, indicative of a potential limitation for the use auto-selection of hot and cold pixels when using the UAS-METRIC model

UAS in agriculture. Mid-sized UAS

In recent years, the agribusiness industry has been trying to keep pace with rapid developments, one of which is in the sector of small unmanned aerial systems (UASs). These systems have gained the attention of growers and researchers alike. Undeniably, the reach of this technology in agricultural decision making is only limited by the imagination. We must look beyond small UAS to realize the full potential of UAS technologies in precision agriculture. This publication describes the domain of mid-sized UAS with pertinent discussions on their suitable use, including case-study scenarios of such in agricultural production management

Assessing Suitability of Auto-Selection of Hot and Cold Anchor Pixels of the UAS-METRIC Model for Developing Crop Water Use Maps

The METRIC energy balance model uses an auto-selection approach for identifying hot (dry, bare soil) and cold (fully transpiring crop) anchor pixels for the internal calibration of the model. When an unmanned aerial system (UAS) is used for imagery, the small image size and the varying crop and soil water status of agricultural fields make the identification of reliable hot and cold pixels challenging. In this study, we used an experimental spearmint field under three irrigation levels (75%, 100%, and 125% of crop evapotranspiration, ETc). As a way of providing diverse field conditions, six different extents (Extent 1 to Extent 6) were selected from each day of the seven days of UAS imagery campaigns of the same field for generating UAS-based ETc maps using auto-selection of hot and cold anchor pixels for the internal calibration of the model. Extent 1 had the smallest coverage area of the field, including only plants that were irrigated at 75% of ETc, while the fields of view of the other extents increased to where the Extent 6 covered the spearmint field and all the surroundings including trees, a nearby water canal, irrigated grass, and irrigated and non-irrigated soil. The results showed that different sizes of extent resulted in the selection of variable hot (bare, but moist soil in small extents, and dry bare soil at the larger extents) and cold anchor pixels (crop under water stress at the small extents, and tree canopy or grass alongside the water canal at the larger extents). This variation resulted in significantly different ETc estimation for the same spearmint crop field, indicative of a potential limitation for the use auto-selection of hot and cold pixels when using the UAS-METRIC model

Six steps to calibrate and optimize airblast sprayers for orchards and vineyards

The goal for all pesticide applications should be to get every drop to\n the crop. Calibration ensures that the appropriate product rate is applied by the\n sprayer, while optimization ensures that the product is delivered onto the intended\n target. Calibrating and optimizing the sprayer are essential to sustainability, as they\n affect the environment, farm workers, and economic impact through fruit quality and\n pesticide costs. In six steps, this publication explains how to measure ground speed and\n nozzle output, check and adjust airflow and nozzle alignment, and verify coverage for an\n airblast sprayer. Methods are outlined for manual completion of the steps, and\n simplified formulas and suggestions for tools that can make the process faster are\n included