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Trunk injection of chemical compounds is a target-specific method of applying therapeutics that increases applied compound efficiency without compromising environmental safety and has proven to be an effective method to control citrus Huanglongbing (HLB). However, conventional (manual) trunk injection methods are labour-intensive and time-consuming, resulting in high application costs and posing implementation challenges for commercial citrus growers. For this reason, an automated, drill-free system for injecting therapeutic materials into citrus trees to control HLB has been developed. The injection mechanism consists of an end effector and a retractable telescoping positioning arm mountable to a farm vehicle. The system employs a metering pump with adjustable maximum pressure and flow rate for injecting therapeutic materials into tree trunks. This novel technology was evaluated in commercial citrus orchards; the applied volume, injection rate, and injection pressure were measured, and differences based on the time of injection, trunk diameter at the injection point, and irrigation status were examined. The injection was performed until the flow stopped; the injection duration ranged from 30 s to 72 s for all trees. The minimum and maximum volume injected was 53.9 mL and 243.6 mL, respectively, at 44 s and 68 s. The average injection rate per tree ranged from 1.23 mL s−1 to 3.58 mL s−1, and cut-off pressure ranged from 0.97 MPa to 2.01 MPa. • An automated drill-free mechanism for rapid trunk injection has been developed. • The device is mountable to farm vehicles to supply therapeutic materials to trees. • This novel technology was evaluated in commercial citrus orchards. • This is the first automated trunk injection system developed for tree crops. [ABSTRACT FROM AUTHOR]

Trunk injection of chemicals is an alternative to conventional methods of applying plant protection products, and it can potentially deliver the exact dosage of therapeutic material required by the plant without undermining environmental protection and human health. Although trunk injection has proven to be an effective method for managing systemic pathogens, several studies have highlighted limitations for large-scale commercial tree crop applications because of the manual process involved, emphasising the need for an automated injection system. The process of trunk injection involves the steps of hole creation on the main stem of a tree and the injectate delivery of the therapeutic material. Injection mechanisms can be categorised based on the approach of creating an injection port, method of injectate delivery, and pressure source used to apply the injected material. This review article extensively investigates the development of various trunk injection mechanisms, operating principles, and practical applications, critically presenting advances in tree injections and highlighting gaps in knowledge to guide the development of automated injection systems. This review also discusses optimum injection parameters, such as hole size, above-ground injection height, number of injection ports, injection rate, and pressure. Additionally, it covers other factors such as tree variability, leaks and backflow, and the question of air exclusion during injection and wounding that may contribute to the design of an effective injection device. Furthermore, recent advances in developing an automated injection device are highlighted. • Trunk injection mechanisms' development and operating principles were investigated. • Optimum injection parameters were discussed. • Advances in tree injections and gaps in knowledge are highlighted. • Limitations of current technologies and potential damage to trees are discussed. [ABSTRACT FROM AUTHOR]

Heat treatment has been applied in previous studies to treat diseased plants and trees affected by heat-sensitive pathogens. Huanglongbing (HLB) is a heat-sensitive pathogen and the optimal temperature-time for treating HLB-affected citrus trees was estimated to be 54 °C for 60 to 120 s from indoor experimental studies. However, utilizing this method in orchards is difficult due to technical difficulties to effectively apply heat. Recently, a mobile thermotherapy system (MTS) was developed to in-field treat HLB-affected trees. This mobile device includes a canopy cover that covers the diseased tree and a system to supply steam under the cover to treat the tree. It was proven that the temperature inside the canopy cover can reach the desired one (i.e., 54 °C) to kill bacteria. However, for HLB, the heat should penetrate the tree's phloem where the bacteria live. Therefore, measuring the heat penetration inside the tree is very critical to evaluate the performance of the MTS. In this study, a heat transfer model was developed to simulate the heat penetration inside the tree and predict the temperature in the phloem of the diseased tree during the in-field heat treatment. The simulation results were compared with in-field experimental measurements. The heat transfer model was developed by a comparative analysis of the experimental data using the ANSYS software. Results showed that the temperature in the phloem was 10-40% lower than the temperature near the surface of the bark. Simulation results were consistent with experimental results, with an average relative error of less than 5%. [ABSTRACT FROM AUTHOR]

Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans. In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions. [ABSTRACT FROM AUTHOR]

In this study hyperspectral imaging (380–1020 nm) and machine learning were utilised to develop a technique for detecting different disease development stages (asymptomatic, early, intermediate, and late disease stage) of powdery mildew (PM) in squash. Data were collected in the laboratory as well as in the field using an unmanned aerial vehicle (UAV). Radial basis function (RBF) was used to discriminate between healthy and diseased plants, and to classify the severity level (disease stage) of a plant; the most significant bands to differentiate between healthy and different stages of disease development were selected (388 nm, 591 nm, 646 nm, 975 nm, and 1012 nm). Furthermore, 29 spectral vegetation indices (VIs) were tested and evaluated for their ability to detect and classify healthy and PM-infected plants; the M value was used to evaluate the VIs. The water index (WI) and the photochemical reflectance index (PRI) were able to accurately detect and classify PM in asymptomatic, early, and late development stages under laboratory conditions. Under field conditions (UAV-based), the spectral ratio of 761 (SR761) accurately detected PM in early stages, and the chlorophyll index green (CI green), the normalised difference of 750/705 (ND 750/705), the green normalised difference vegetation index (GNDVI), and the spectral ratio of 850 (SR850) in late stages. The classification results, by using RBF, in laboratory conditions for the asymptomatic and late stage was 82% and 99% respectively, while in field conditions it was 89% and 96% in early and late disease development stages, respectively. • A lab- and UAV-based powdery mildew (PM) disease detection system was developed. • Spectral reflectance analysis of PM-infected squash leaves was performed. • The most significant wavelengths and vegetation indices were selected. • The technique was able to detect different stages of PM progress in squash. [ABSTRACT FROM AUTHOR]

Highlights • A Grapevine yellows (GY) disease detection system was developed. • It can distinguish GY disease from other diseases with similar symptoms. • Six neural network architectures were utilized and evaluated. • This automatic tool is crucial to avoid missing GY-positive plants. • It offers an end-to-end detection of GY. Abstract Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY's primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease. [ABSTRACT FROM AUTHOR]

Highlights • A Laurel Wilt detection disease system was developed. • It can distinguish Laurel Wilt disease from other disorders with similar symptoms. • Early disease detection was achieved utilizing hyperspectral data. • Spectral signature reflectance and vegetation indices were used for disease detection. Abstract Laurel wilt (Lw) disease is an exotic and lethal disease that can kill laurel family trees very fast. It is vectored by the redbay ambrosia beetle that prefers to live and lay eggs inside avocado trees (among other plants). Lw disease continues to expand in Florida posing a major threat to the avocado industry. Early and accurate disease detection is very critical in this case to remove infected trees and distinguish Lw disease from other diseases or disorders with similar symptoms. Herein, we present a nondestructive remote sensing method to detect Lw-infected avocado trees (in early and late stage) and discriminate them from healthy and other factors that cause similar symptoms, such as iron and nitrogen deficiencies, by using a portable spectral data collection system (visible – near infrared; 400–970 nm). Two data sets were collected in 10 nm and 40 nm spectral resolution, and 23 vegetation indices (VIs) were calculated to detect Lw-affected trees by using two classification methods: decision tree (DT) and multilayer perceptron (MLP) neural networks. Additionally, the optimal wavelengths and VIs to discriminate healthy, Lw-infected and avocado trees with iron and nitrogen deficiencies were identified. The results showed that it was possible to detect Lw-infected trees at early stage and distinguish them from other biotic and abiotic factors with high accuracy (around 100%) using the MLP method. Poorer results were achieved with DTs. The optimum 10 nm wide bands and VIs selected for the Lw-detection were found in the red, red-edge and NIR bands. [ABSTRACT FROM AUTHOR]

Florida is the largest fresh tomato producer in the USA. Due to the subtropical climate, especially the hot and humid weather, the devastating tomato bacterial spot disease (TBS), caused by the bacteria Xanthomonas perforans , has been a threat to tomato crops for decades. Prevention through early disease detection is a crucial management strategy to minimise losses. Diagnostics for this disease that are commonly used are a visual diagnosis of disease symptoms or laboratory assays. These procedures are time-consuming, require specialised personnel, and are inefficient in detecting the disease in its early stages. Hyperspectral imaging provides a non-invasive tool for early disease detection in various crops. In this study, five optimised machine learning algorithms (MLAs), such as Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLSDA), Weighted K-Nearest Neighbours (W–KNN), Support Vector Machine (SVM), and Ensemble Boosting Tree (EBT), were utilised for early detection of TBS in transplant houses. Two normalisation procedures, a standardisation method, and unit vectorisation, were applied to preprocess the data in a dynamic range expansion process. The input variables to the models included time-lined events of disease progression and reduced datasets. To create the reduced datasets, a novel data reduction methodology was developed, which was able to identify 12 key (significant) wavelengths (from the 300 initial scans of the hyperspectral camera) that carry the highest weighted spectral components for accurately detecting TBS-affected plants. The PLSDA model presented the highest F1 score (90%) on early detection of TBS. • Early detection of bacterial spot disease was achieved in tomato transplant houses. • Optimized machine learning algorithms and hyperspectral imagery were utilized. • A novel data reduction technique identified 12 significant wavelengths. • A low-cost optical device for monitoring transplant houses can be developed. [ABSTRACT FROM AUTHOR]

• A feature extraction method for identifying plant diseases was developed. • Karhunen-Loeve Expansion was used to identify patterns in spectral data. • This method can define a reduced spectral dataset for early-stage disease detection. • It can be used in classical and deep-learning methods for classifying diseases. This study introduces a novel method using reduced datasets for extracting plant reflectance signatures. Plant reflectance data from hyperspectral imaging sensors were used to develop biomarker signatures for plant identification and early disease detection purposes. The Karhunen-Loeve Expansion (KLE) of spectral reflectance data, taken from healthy and diseased plants (two case studies of citrus canker in citrus and Laurel wilt in avocado), were used to identify a basis set of functions that represents the distribution of the reflected signal energy. By spectral decomposition, the eigenvalues were related to the KLE basis set. The eigenvalues can be used to identify the KLE eigenvectors, which comprise the highest variation in the data. These components can be interpreted as the weighted variables which carry with them most of the information on the reflectance spectrum of the plant. From indications presented by this multivariate KLE analysis, a frequency reconstruction was adapted to convert the eigenvector information to a wave function. This reconstruction via KLE and frequency transformation formed the signature identification process (i.e., unique biomarker). These frequency spectra can be used as average signature reflectance patterns for plant identification, classification, and disease biomarkers. Defining these spectral identification biomarkers or signatures is purposeful since it could lead to less invasive classification and disease diagnostics techniques. The techniques used to determine these reflectance spectra require a unique spectrum reconstruction method, using Fourier transform methods and KLE for data reduction. [ABSTRACT FROM AUTHOR]

• This study provides a rapid and low-cost approach for strawberry bruise inspection. • Two lighting conditions were applied respectively for image acquisition. • Four bruise severities were classified based on the Bruise Ratio. • UV light was more useful in classifying strawberry bruise severities. • The AI-based strawberry bruise detection system can be applied for commercial use. Bruising is one of the major defects of strawberries. Strawberry bruised areas are often visible as flattened, sunken, and discolored. Postharvest strawberries can be bruised by compression, impact, or vibration forces during harvesting, transportation, and packinghouse operations. These bruises make strawberries more vulnerable to rot or disease, thus shortening their shelf life. Bruises can also cause consumers to abandon purchase decisions. Therefore, inspecting strawberry bruises is an important procedure for commercial strawberry farms to guarantee the high quality of strawberries sent to the market. A novel technique was developed in this study to detect bruises on strawberry images captured by a color camera under incandescent and ultraviolet (UV) light. Mask Region-Based Convolutional Neural Networks (Mask R-CNN), a deep learning method, was utilized for the strawberry bruise detection. Four bruise severities were classified according to the estimated bruise ratio of every single strawberry (Minor Bruise, Light Bruise, Moderate Bruise, and Severe Bruise). The results showed that Mask R-CNN could accurately detect whether a strawberry is bruised or not under both lighting conditions. However, the strawberry bruise severity classification accuracy of the proposed machine vision technique was improved significantly under UV light compared to incandescent light. For whole strawberry bruise detection under UV light, the F1 score for detecting strawberry bruises was 0.99, and the highest F1 score for whole strawberry bruise severity classification was 0.92 for Minor Bruise. This strawberry bruise detection system can assist farmers in detecting bruised strawberries on the processing line or an automated strawberry harvester, improve the quality of strawberries that are sent to the market, and avoid shortening the shelf life of fresh strawberries. [ABSTRACT FROM AUTHOR]

The harvest process for specialty crops is generally one of intensive activity because many people are required for harvest and packing, and the harvest window is brief due to the high perishability of the produce. Herein we present a cloud-based Harvest Management Information System (HMIS) that combines a novel real-time Portable Labor Monitoring System (PLMS) with a cloud-based harvest management software. The PLMS comprised of three key elements (1) a self-leveling scale, (2) electronic control box, and (3) a frame that supports all hardware. The electronic control box includes: (i) a RFID reader, (ii) a LCD display, (iii) a thermal printer, (iv) a GPS module, and (v) a communication system. RFID tags, containing unique ID numbers, embedded within rubber wrist bands, are worn by pickers. This system can read a picker’s ID (RFID bracelet), measure the weight of fruit, and record the time and location (optional) of every fruit ‘transaction’ (i.e., every time a picker brings a bucket of fruit to the collection bin). The collected data can be transmitted wirelessly to the server in real-time. The cloud-based software receives and processes the PLMS data on labor activities, visualizes the collected data, and can extract the data necessary for management information and automated filling of documents (e.g. payroll, yield maps). The HMIS is unique in its ability to: (1) accurately credit pickers for the fruit they have harvested in the field without impeding or altering the harvest process, (2) streamline data entry to payroll, (3) provide real-time tracking of harvest, yield mapping, and traceability, and, (4) generate precise and reliable harvest efficiency data. This integrated system was evaluated in sweet cherry, blueberry and apple orchards in Washington, USA. The weight of harvested fruit, time and location of every fruit drop were calculated accurately; all the data were transmitted wirelessly to the server and no errors were recorded. [ABSTRACT FROM AUTHOR]

Floral organ attributes are important for pollinator attraction and pollination success in many plants. As part of a broader effort investigating causes for variable fruit set among sweet cherry ( Prunus avium L.) genotypes, we have studied the role of temperature on floral organs in cultivars exhibiting high (‘Sweetheart’ and ‘Rainier’) or low fruit set (‘Benton’ and ‘Tieton’) in field conditions. In 2010 and 2011, two-year old sweet cherry limbs collected at ‘tight cluster’ flower stage were distributed randomly among three controlled environment chambers programmed to mimic a warm, moderate, or cool blooming period. Entire flowers were sampled and dissected at ‘tight cluster’, ‘first white’, ‘half white’, ‘first open’ and ‘full open’ stages. There was high year-to-year variability in size of all organs except stylar and filament lengths. ‘Tieton’ floral organs were significantly larger than other cultivars with the exception of filament, style and pedicel lengths. Between the earliest and the latest developmental stages petal area enlarged ca. 4.5 times, filament length increased ca. 3 times and styles doubled in length, across all cultivars. Irrespective of cultivar, the lengths of the styles and filaments were the most sensitive to temperature, being about 11% and 25% shorter in the low temperature environment compared to high temperature, respectively. This suggests that temperatures after the ‘tight cluster’ stage play an important role in the growth of styles and filaments. In addition, interestingly, our current results revealed that low productivity cultivars, i.e. ‘Tieton’ and ‘Benton’, have relative short styles to its petal size compared to the other two high productivity cultivars. [ABSTRACT FROM AUTHOR]

Harvest costs are generally the single greatest expense for specialty crop producers. Identifying and reducing inefficiencies during harvest are essential steps towards reducing costs and maintaining product quality. To streamline harvest operations, the number of workers and machines required to harvest, handle, and transport the product needs to be planned along with the execution of field operations. In this paper the fruit harvest and the bin collection process are modelled, adopting a modified machine repair model (machine interference problem) from the operations research area. An algorithm was developed in Matlab® to evaluate the performance of the system and improve confidence in sizing the fleet (workers and machines). Two different case studies are modelled using this algorithm: i) manual table grape harvest in Greece, and ii) manual sweet cherry harvest in Washington State, USA. First, the harvest procedures are described, and then the machine repair model is formulated to model each process. The bin loading process during grape harvest in Greece, and the sweet cherry picking process in USA are simulated. System performance under different number of bin carriers (or pickers) and loading stations is evaluated and results are presented. [ABSTRACT FROM AUTHOR]

Intuitively, tree architecture will affect harvest efficiency of tree fruit crops, yet there are no empirical studies that document this. The objective of the current research was to investigate the role of training system on harvest rate of individual pickers in commercial sweet cherry (Prunus avium L.) orchards. We used a real-time labor monitoring system (LMS) with the ability to track and record individual picker efficiency in 11 orchards throughout the Pacific Northwest. Trees were trained to one of five different architectures: 1) upright fruiting offshoots (UFO), a planar architecture comprised of unbranched vertical fruiting wood; 2) Y-trellised, an angled dual planar architecture; 3) Kym Green Bush (KGB), a multileader bush; 4) central leader (CL); and 5) traditional multileader open center (MLOC), trees comprised of three to five main leaders. A consistent picking crew was used to facilitate comparisons among systems and eliminate variability in skill among pickers. The LMS calculated harvest rate, picking cost, weight of harvested fruit, number of harvested buckets, range in fruit weight per bucket/bin, and mean fruit weight per bucket/bin for individual pickers. Tests revealed a significant effect of canopy architecture on labor efficiency. The highest mean (± SE) harvest rates (0.94 ± 0.02 kg⋅min-1 and 0.78 ± 0.03 kg⋅min-1) were recorded in 'Cowiche'/'Gisela'®5' and 'Tieton7'Gisela®5' orchards trained to the UFO system, respectively. High harvest efficiency in these orchards was likely the result of the planar, simplified architecture and that most fruit were accessible from the ground. The third highest picking rate was recorded in the KGB system (0.73 ± 0.04 kg⋅min-1), a fully pedestrian orchard. Interestingly, harvest rate of slower pickers was improved to a greater extent (+132%) than skilled pickers (+83%) when comparing pedestrian and planar systems (e.g., UFO and KGB) with traditional architecture (MLOC). Furthermore, picking rate of individual pickers varied within 1 day by more than 100%, likely as a result of variability in fruit density within trees, tree size as well as fruit accessibility. We documented variability of more than 35 kg in final bin weight across all orchards and a range in bucket weight between ≈7 and 13 kg. These results suggest that architecture has a major effect on harvest efficiency and that current systems of piece-rate picker reimbursement are beset with inaccuracy. [ABSTRACT FROM AUTHOR]

Harvest costs are significant for tree fruit producers and, yet, not well studied. In this paper a prototype system for measuring harvest worker efficiency in sweet cherry ( Prunus avium L.) is presented. This weighing system consists of a digital weighing platform compatible with standard commercial fruit bins and a data logger interfaced to a wireless radio. Weight data were transmitted every 5 s, filtered and stored to a laptop computer (database). System functionality and reliability were evaluated in five orchards in cooperation with three commercial growers. Preliminary tests showed that the system did not interfere with normal harvest activities and that the efficiency of pickers and picking teams varied within and across orchard blocks, (e.g. 0.75 to 2.87 kg/min), depending on their experience and skills (e.g. 1.37 kg/min for skilled workers vs. 0.64 kg/min for unskilled). Further, the mean picking rate of 'Skeena' without pedicels (stem-free) was 1.15 kg/min, almost 50 % greater than fruit with stems (mean = 0.75 kg/min) when picked by the same crew and orchard. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a real-time labor monitoring system (LMS) with the ability to track and record individual picker efficiency during manual harvest of specialty crops. This system utilizes existing commercial harvest equipment and integrates a digital weighing scale, RFID reader, computational unit, and a portable datalogger carried by pickers. The RFID reader, digital scale and computational unit are assembled on a common portable chassis. As pickers transfer fruit into a standard collection bin, the system reads the picker’s ID (RFID tag) and the weight of fruit. Weight data can then be transmitted wirelessly to the picker’s datalogger which records and displays the incremental and total weight of harvested fruit. An algorithm was developed in Matlab® to record, process and store the data, as well as to transmit wirelessly the weight value to the wearable datalogger. System prototypes were assembled and field-tested for accuracy and reliability during commercial harvest of sweet cherries (Prunus avium L.) in the Pacific Northwest. The LMS reliably calculated the harvest rate, picking cost, weight of harvested fruit, number of harvested buckets, range in fruit weight per bucket, and mean fruit weight per bucket, in real time. The mean harvest rate (±standard error) in a ‘Chelan’/Mazzard sweet cherry orchard trained to a steep leader architecture was 0.53±0.13kg/person/min. Harvest rate was similar for the same genotype trained to a steep leader (3 leaders) at 0.50±0.10kg/person/min and for harvest of ‘Tieton’/‘Gisela®6’ trees trained to a central leader architecture (0.53±0.15kg/person/min). When surveyed after using the LMS, every picker indicated a preference for knowing the exact weight of fruit they harvested compared with the current system of reimbursement per bucket or bin. Using the LMS, reliable data on worker efficiency can be collected with minimal interference with standard commercial practices. [Copyright &y& Elsevier]

• A novel wearable technology was developed for horse foraging behavior analysis. • It utilizes sound recognition techniques and a recurrent neural network (RNN). • It can accurately detect and distinguish bite and chew events in horses. • It can provide valuable information to predict pasture use and animal performance. Wearable sensing technologies can be used for precision livestock production and to study foraging strategies to better understand the relationships between herbivores, vegetation, and landscape. In this context, monitoring grazing behavior (i.e. chew and bite events) can provide critical information for livestock management. This study presents a computational tool that utilizes wearable sensing and deep learning to distinguish chew and bite events in horses. A micro camera equipped with a microphone (0–18 kHz) was used to obtain video/audio data from horses during grazing. The collected audio data were treated in a pre-processing filtering step, then used to train a recurrent neural network (RNN) with a long short-term memory (LSTM) layer to detect and distinguish chews, bites, and noise events. A post-processing sliding window technique was used to filter events with low confidence levels and lengths. Initial evaluation of this system showed an accuracy of 88.64% for bite identification and an accuracy of 94.13% for chew identification. The distinction between events and evaluation of responses to different pasture species and structure can provide useful information on the plant-animal interface. That is aligned to information such as bite rate, bite mass, and grazing time, and can help determine management strategies that optimize intake and provide data for modeling foraging behavior to predict pasture use and animal performance. [ABSTRACT FROM AUTHOR]

• An automated system was developed for measuring pecan nut growth. • It utilizes machine vision and deep learning convolutional neural networks. • It provides rapid and precise measurements of different pecan nut parts throughout the season. • It can be used for more precise management of irrigation, pesticide, and harvest schedules. Knowing pecan nut growth dynamic and its thermal requirements from bloom to ripening are essential to predict nut size and harvest date. However, monitoring of pecan growth (shuck, shell, and embryo) is a time consuming manual task, requiring multiple measurements for each nut. This process also ignores any deformation of the nut. A computer vision system can facilitate such a task by performing pixel-by-pixel semantic segmentation for all parts of the nut. This study uses the Mask-RCNN algorithm for object detection and semantic segmentation for area estimation of shuck, shell, and embryo on pecans in multiple growth stages. The dataset was divided in two stages: small (young) and big (older) pecans. The network chosen achieved an F1 score of 95.3% to 100% on the models for the object detection task. The area estimation achieved a mean absolute percentage error of 10.14% to 28.06% on these tasks. The resulting growth plot presents a sigmoid curve for all parts of the nut, which demonstrates the precision of the measurement. This automated measuring system makes it possible to have a multidimensional understanding of each aspect of nut development, and gives us the ability of modeling nut growth phenology and predicting nut productions. [ABSTRACT FROM AUTHOR]

Nutrient assessment of plants, a key aspect of agricultural crop management and varietal development programs, traditionally is time demanding and labor-intensive. This study proposes a novel methodology to determine leaf nutrient concentrations of citrus trees by using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence (AI). The study was conducted in four different citrus field trials, located in Highlands County and in Polk County, Florida, USA. In each location, trials contained either 'Hamlin' or 'Valencia' sweet orange scion grafted on more than 30 different rootstocks. Leaves were collected and analyzed in the laboratory to determine macro- and micronutrient concentration using traditional chemical methods. Spectral data from tree canopies were obtained in five different bands (red, green, blue, red edge and near-infrared wavelengths) using a UAV equipped with a multispectral camera. The estimation model was developed using a gradient boosting regression tree and evaluated using several metrics including mean absolute percentage error (MAPE), root mean square error, MAPE-coefficient of variance (CV) ratio and difference plot. This novel model determined macronutrients (nitrogen, phosphorus, potassium, magnesium, calcium and sulfur) with high precision (less than 9% and 17% average error for the 'Hamlin' and 'Valencia' trials, respectively) and micro-nutrients with moderate precision (less than 16% and 30% average error for 'Hamlin' and 'Valencia' trials, respectively). Overall, this UAV- and AI-based methodology was efficient to determine nutrient concentrations and generate nutrient maps in commercial citrus orchards and could be applied to other crop species. [ABSTRACT FROM AUTHOR]

Tomato crops are susceptible to multiple diseases, several of which may be present during the same season. Therefore, rapid disease identification could enhance crop management consequently increasing the yield. In this study, nondestructive methods were developed to detect diseases that affect tomato crops, such as bacterial spot (BS), target spot (TS), and tomato yellow leaf curl (TYLC) for two varieties of tomato (susceptible and tolerant to TYLC only) by using hyperspectral sensing in two conditions: a) laboratory (benchtop scanning), and b) in field using an unmanned aerial vehicle (UAV-based). The stepwise discriminant analysis (STDA) and the radial basis function were applied to classify the infected plants and distinguish them from noninfected or healthy (H) plants. Multiple vegetation indices (VIs) and the M statistic method were utilized to distinguish and classify the diseased plants. In general, the classification results between healthy and diseased plants were highly accurate for all diseases; for instance, when comparing H vs. BS, TS, and TYLC in the asymptomatic stage and laboratory conditions, the classification rates were 94%, 95%, and 100%, respectively. Similarly, in the symptomatic stage, the classification rates between healthy and infected plants were 98% for BS, and 99–100% for TS and TYLC diseases. The classification results in the field conditions also showed high values of 98%, 96%, and 100%, for BS, TS, and TYLC, respectively. The VIs that could best identify these diseases were the renormalized difference vegetation index (RDVI), and the modified triangular vegetation index 1 (MTVI 1) in both laboratory and field. The results were promising and suggest the possibility to identify these diseases using remote sensing. [ABSTRACT FROM AUTHOR]

Quantifying certain physiological traits under heat-stress is crucial for maximizing genetic gain for wheat yield and yield-related components. In-season estimation of different physiological traits related to heat stress tolerance can ensure the finding of germplasm, which could help in making effective genetic gains in yield. However, estimation of those complex traits is time- and labor-intensive. Unmanned aerial vehicle (UAV) based hyperspectral imaging could be a powerful tool to estimate indirectly in-season genetic variation for different complex physiological traits in plant breeding that could improve genetic gains for different important economic traits, like grain yield. This study aims to predict in-season genetic variations for cellular membrane thermostability (CMT), yield and yield related traits based on spectral data collected from UAVs; particularly, in cases where there is a small sample size to collect data from and a large range of features collected per sample. In these cases, traditional methods of yield-prediction modeling become less robust. To handle this, a functional regression approach was employed that addresses limitations of previous techniques to create a model for predicting CMT, grain yield and other traits in wheat under heat stress environmental conditions and when data availability is constrained. The results preliminarily indicate that the overall models of each trait studied presented a good accuracy compared to their data's standard deviation. The yield prediction model presented an average error of 13.42%, showing the function-on-function algorithm chosen for the model as reliable for small datasets with high dimensionality. [ABSTRACT FROM AUTHOR]

• A cloud- and AI-based application was developed to analyze and visualize UAV-collected data. • This interactive and user-friendly application can automatically create tree inventories and assess plant characteristics. • It simplifies the data processing and visualization procedures and promotes adoption of UAVs. • It can provide a low-cost, consistent, and rapid method for field survey and plant phenotyping. Traditional sensing technologies in specialty crops production, for pest and disease detection and field phenotyping, rely on manual sampling and are time consuming and labor intensive. Since availability of personnel trained for field scouting is a major problem, small Unmanned Aerial Vehicles (UAVs) equipped with various sensors can simplify the surveying procedure, decrease data collection time, and reduce cost. To accurate and rapidly process, analyze and visualize data collected from UAVs and other platforms (e.g. small airplanes, satellites, ground platforms), a cloud and artificial intelligence (AI) based application (named Agroview) was developed. This interactive and user-friendly application can: (i) detect, count and geo-locate plants and plant gaps (locations with dead or no plants); (ii) measure plant height and canopy size (plant inventory); (iii) develop plant health (or stress) maps. In this study, the use of this Agroview application to evaluate phenotypic characteristics of citrus trees (as a case study) is presented. It was found, that this emerging technology detected citrus trees with mean absolute percentage error (MAPE) of 2.3% in a commercial citrus orchard with 175,977 trees (1,871 acres; 39 normal and high-density spacing blocks). Furthermore, it accurately estimated tree height with 4.5% and 12.93% MAPE for normal and high-density spacing respectively, and canopy size with MAPE of 12.9% and 34.6% for normal and high-density spacing respectively. It provides a consistent, more direct, cost-effective and rapid method for field survey and plant phenotyping. [ABSTRACT FROM AUTHOR]

• A UAV-based high-throughput technique was developed for citrus tree. • Phenotypic characteristics of sweet orange trees grafted on 25 rootstock were evaluated. • Data collected by UAV were correlated significantly with manually collected data. • This cost-efficient tool can be used for monitoring phenotypic changes of plants at spatial and temporal resolution. • It provides an objective, accurate, and cost-efficient approach for plant monitoring and health assessment. The implementation of breeding methods requires the creation of a large and genetically diverse training population. Large-scale experiments are needed for the rapid acquisition of phenotypic data to explore the correlation between genomic and phenotypic information. Traditional sensing technologies for field surveys and field phenotyping rely on manual sampling and are time consuming and labor intensive. Since availability of personnel trained for phenotyping is a major problem, small UAVs (unmanned aerial vehicles) equipped with various sensors can simplify the surveying procedure, decrease data collection time, and reduce cost. In this study, we evaluated the phenotypic characteristics of sweet orange trees grafted on 25 rootstock cultivars with different influences on plant growth and productivity utilizing a UAV-based high throughput phenotyping system. Data collected by UAV were compared with data collected manually according to standard horticultural procedures. The UAV-based technique was able to detect and count citrus trees with high precision (99.9%) in an orchard of 4931 trees and estimate tree canopy size with a high correlation (R = 0.84) with the manual collected data. No correlation of UAV-based data and manually collected data was observed for yield. The reason for the observed deviation is the influence of different rootstock cultivars on yield efficiency. Despite the low vigor-inducing effect of some rootstocks, they are highly productive, whilst others are high in vigor but produce less fruit. Our study demonstrates the high accuracy of the UAV technique to assess tree size. When using these techniques, it is essential to recognize the limitations imposed by the biological system. [ABSTRACT FROM AUTHOR]

Identifying and quantifying the number and size of stomata on leaf surfaces is useful for a wide range of plant ecophysiological studies, specifically those related to water-use efficiency of different plant species or agricultural crops. The time-consuming nature of manually counting and measuring stomata have limited the utility of manual methods for large-scale precision agriculture applications. A deep learning segmentation network was developed to automate the analysis of stomatal density and size and to distinguish between open and closed stomata using citrus trees grafted on different rootstocks as a model system. A novel method was developed utilizing the Mask-RCNN algorithm, which allows identification, quantification, and characterization of stomata from leaf epidermal peel microscopic images with an accuracy of up to 99%. Moreover, this method permits the differentiation of open and closed stomata with 98% precision and measurement of individual stomata size. In the citrus model system, significant differences in the size and density of stomata and diurnal regulation patterns were detected that were associated with the rootstock cultivar on which the trees were grafted. Nearly 9000 individual stomata were analyzed, which would have been impractical using manual methods. The novel automated method presented here is not only accurate, but also rapid and low-cost, and can be applied to a variety of crop and non-crop plant species. [ABSTRACT FROM AUTHOR]

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks. [ABSTRACT FROM AUTHOR]

This paper examines the effects of oil prices on home values in Kern County, California's top oil producer. Using monthly data from 1990:01 to 2018:03, results from an ARDL model indicate that there is a long-run equilibrium relationship between oil prices, unemployment, interest rates, and home values. In the short run, a 1% increase in unemployment and interest rates will decrease home values by 2.06 and 0.82%, respectively. VEDC and GIRFs imply that changes in Kern's home values will influence home prices in San Bernardino County. Los Angeles has the greatest effect on home sales in Kern County. [ABSTRACT FROM AUTHOR]

Highlights • A smart sprayer for precision pest management was developed. • This low-cost technology can distinguish target weeds from non-target objects. • It sprays only on a selected target (weed). • Neural networks were utilizing for target detection and classification. • It can significantly reduce the quantity of agrochemicals applied. Abstract Most conventional sprayers apply agrochemicals uniformly, despite the fact that distribution of weeds is typically patchy, resulting in wastage of valuable compounds, increased costs, crop damage risk, pest resistance to chemicals, environmental pollution and contamination of products. To reduce these negative impacts, a smart sprayer was designed and developed utilizing machine vision and artificial intelligence to distinguish target weeds from non-target objects (e.g. vegetable crops) and precisely spray on the desired target/location. Two different experimental scenarios were designed to simulate a vegetable field and to evaluate the smart sprayer's performance. The first scenario contained artificial weeds (targets) and artificial plants (non-targets). The second and most challenging scenario contained real plants; portulaca weeds as targets, and sedge weeds and pepper plants as non-targets. Two different embedded graphics processing unit (GPU) were evaluated as the smart sprayer processing unit (for image processing and target detection). The more powerful GPU (NVIDIA GTX 1070 Ti) achieved an overall precision of 71% and recall of 78% (for plant detection and target spraying accuracy) on the most challenging scenario with real plants, and 91% accuracy and recall for the first scenario with artificial plants. The less powerful GPU (NVIDIA Jetson TX2) achieved an overall precision and recall of 90% and 89% respectively on the first scenario with artificial plants, and 59% and 44% respectively on the second scenario with real plants. Finally, an RTK GPS was connected to the smart sprayer and an algorithm was developed to automatically generate weed maps and visualize the collected data (after every application). This smart technology integrates a state of the art (AI-based) weed detection system, a novel fast and precision spraying system, and a weed mapping system. It can significantly reduce the quantity of agrochemicals required, especially compared with traditional broadcast sprayers that usually treat the entire field, resulting in unnecessary application to areas that do not require treatment. It could also reduce costs, risk of crop damage and excess herbicide residue, as well as potentially reduce environmental impact. [ABSTRACT FROM AUTHOR]

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems. [ABSTRACT FROM AUTHOR]

Precision agriculture has emerged as a dominant force in the United States, with widespread adoption of advanced technologies and decision support systems (DSS) since the 1980s. Key tools such as variable rate application (VRA), autopilot systems, and remote sensing have become integral for U.S. farmers, offering invaluable insights from crop, soil, and weather information to optimize agricultural production while minimizing environmental impact. To synthesize and categorize the extensive research available on precision agriculture, a systematic review protocol has been designed. Our objective is to offer clear and authoritative insights into the nature, scope, and volume of this field. Implementing a rigorous search strategy, we utilized renowned databases such as Scopus® and Web of ScienceTM to gather relevant and significant materiality. The retrieval process involved the use of indexing terms and Boolean operators, with a focus on 'precision agriculture' and 'precision farming', striking a balance between specificity and comprehensiveness. To ensure the credibility of our findings, only peer-reviewed papers authored by individuals affiliated with U.S. institutions have been included. Expert reviewers with deep knowledge in the field independently assessed the selected papers, thoroughly evaluating titles, abstracts, keywords, methods, conclusions, and declarations. Consistency and eligibility were paramount in determining which papers met the criteria for inclusion. Any discrepancies or disagreements were resolved through rigorous consensus-building discussions among the reviewers. Through this comprehensive meta-review, we provide a scientific contribution that enhances our understanding of precision agriculture, highlighting focus areas for further research and development (R&D). By synthesizing and categorizing the existing literature, we offer authoritative insights into the research landscape, informing future investigations and fostering innovation. Focusing specifically on the U.S., we shed light on the unique aspects and pioneering advancements in precision agriculture within the country. Ultimately, our findings have the potential to drive progress, contributing to sustainable development, increased productivity, enhanced environmental sustainability, and responsible agricultural practices. [ABSTRACT FROM AUTHOR]

• A smart sensing system for tree crop sprayers was developed and evaluated. • It utilizes sensor fusion (e.g., LiDAR, machine vision, GPS) and artificial intelligence. • The low-cost prototype was evaluated in citrus orchards. • It was able to detect trees and spray based on tree size and leaf density. • It reduced spraying volume by 28% compared to conventional spraying. Delivering the appropriate amount of chemicals for each plant based on their needs is essential for reducing costs, waste, and labor. Smart agricultural machinery can provide variable-rate technologies using artificial intelligence (AI) and machine vision to optimize spraying applications. In this research, a low-cost smart sensing system for controlling airblast tree crop sprayers was designed and evaluated using citrus as a case study. The prototype comprised a LiDAR, machine vision, GPS, flow meters, sensor fusion, and AI to scan trees for tree height, tree classification, and fruit counting. Specifically, this smart sensing system can detect and classify objects to tree or non-tree (e.g., human, field constructions), measure tree height and canopy density, and detect and count fruit. Based on this information, it controls spraying nozzles to optimize spraying applications. A novel software was written in C++ and ran on an Nvidia Jetson Xavier NX embedded computer to process and control the data utilizing data fusion and AI techniques. The smart sensing system's results for tree height estimate indicated a relatively low average error of 6%. A convolutional neural network (CNN) was used to perform tree classification with an average accuracy of 84% in classifying the collected imagery into mature, young, dead, and non-tree objects. The fruit count module (also a CNN) had an F1 score of 89%, compared to ground-truth labeled images of mature and immature citrus fruits. Finally, the adoption of this new sensing method reduced spraying volume by 28%, compared to traditional spraying applications. [ABSTRACT FROM AUTHOR]

Machine vision has been increasingly used to address agricultural issues. One such case is corn field harvest losses and image-based object detection approaches, namely image processing, machine learning, and deep learning were investigated to detect and count infield corn kernels, immediately after harvest for combine harvester performance evaluation. A hand-held low-cost RGB camera was used to collect images with kernels of different backgrounds, based on which a 420 images dataset (200, 40, and 180 for training, validation, and testing, respectively) was generated. Three different models for kernel detection were constructed based on image processing, machine learning, and deep learning. For the imaging processing method, the images were preprocessed (color thresholding, graying, and erosion), followed by Hough circle detection to identify kernels. For the machine learning (cascade detector) and deep learning (Mask R-CNN, EfficientDet, YOLOv5, and YOLOX), models were trained, validated, and tested. Experimental results showed the overall performance of the deep learning network YOLOv5 was superior to the other approaches, with a small model size (89.3 MB) and a high model average precision (78.3 %) for object detection. The detection accuracy, undetection rate and F 1 value were 90.7 %, 9.3 %, and 91.1 %, respectively, and the average detection rate was 55 fps. This study demonstrates that the YOLOv5 model has the potential to be used as a real-time, reliable, and robust method for infield corn kernel detection. [ABSTRACT FROM AUTHOR]

• An AI-based smartphone application was developed for pest detection in real-time. • A database was developed for pest data management. • A spatial interpolation method was used to generate spatial distribution maps for site-specific pest management. • This smartphone application worked faster to achieve higher pest detection accuracy compared with the traditional method. Pests can cause huge yield loss in crop production without timely and effective monitoring methods. The traditional method of manually counting pests is time-consuming and causes bias between different observers. Therefore, this study developed an Android-based smartphone application for automated pest counting using a deep learning model (YOLOv7 tiny). This study evaluated the smartphone application using twospotted spider mite (TSSM) detection as a case study and compared it with the manual counting method. The smartphone application records the total number of pests and the GPS coordinates into a MySQL database. The spatial interpolation method was used to generate a spatial distribution map of TSSM in real-time, which could guide growers to apply the right amount of pesticide at the right location. To train the deep learning model for TSSM detection, 3,348 images were collected using three smartphones. The YOLOv7 tiny model was trained at three input image sizes (160x160, 320x320, and 640x640 pixels). The model trained at 320x320 pixels was chosen to deploy on the smartphone application because of the superior performance for both detection accuracy and inference speed. The Android-based smartphone application was installed on two widely used smartphones (Samsung Galaxy S10e and LG Stylo 6) for field testing. The counting accuracy of TSSM motiles (all life stages except eggs) and TSSM eggs using the smartphone application was 78.1% and 75.3%, respectively. The average counting speed of the smartphone application was 233 s per strawberry (trifoliate) leaf. The smartphone application had higher counting accuracy (7.1% higher for TSSM motiles and 12.7% higher for TSSM eggs) and speed (29 s faster per leaf) than manual counting using a magnifying lens. The quick assessment of field conditions with smartphone technology can allow growers to implement quick management tactics and potentially reduce the amount of pesticides to manage TSSM, which can save growers money and protect the environment. [ABSTRACT FROM AUTHOR]

Deficit irrigation has emerged as a viable approach to increase agricultural water productivity. The objectives of this study were to 1) develop water conservation strategies by investigating responses of green bean (Phaseolus vulgaris) and sweet corn (Zea mays var. saccharata) to four irrigation treatments and 2) evaluate the performance of the Decision Support System for Agrotechnology Transfer (DSSAT) model in simulating growth and yield responses of green bean and sweet corn under four irrigation treatments. A field experiment was conducted using 32 experimental plots, established under a linear move sprinkler irrigation system equipped with variable rate irrigation (VRI) technology, at the Tropical Research and Education Center (TREC), Homestead for two cropping seasons between November and March in 2020–2021 and 2021–2022. The experiment involved four irrigation treatments: full irrigation (FI) (T4), and three levels of deficit irrigation, 75% FI (T3), 50% FI (T2), and 25% FI (T1) with four replications arranged in a completely randomized block design (RCBD) for each crop. The average soil moisture content of the full irrigation treatments was maintained above the management allowable depletion (MAD). Field data collection included various parameters of the crops including plant phenology, plant height, canopy width, fresh and dry biomass, and yield. Results confirmed that all three deficit irrigation treatments did not significantly affect the yield and yield components of the two crops during both seasons compared to the T4. This finding revealed that the highest crop water productivity was achieved from T1, 38.3 and 41.4 kg m−3 for green bean and 54 and 53 kg m−3 for sweet corn during the 2020–2021 and 2021–2022 seasons and up to 75% irrigation water saving could be achieved without affecting plant growth and yield. The DSSAT model performed well in simulating yield and yield components for the two crops in response to the four irrigation treatments. Model evaluation results showed that the minimum mean absolute error (MAE) for green bean fresh pod yield simulation was 2.1 Mg ha−1 with an index of agreement (d -Stat) of 0.6, while for biomass, it was 0.24 Mg ha−1 with a d -Stat of 1. Similarly, for sweet corn, the model simulated fresh cob yield with a minimum MAE of 6.51 Mg ha−1 and d -Stat of 0.9 and biomass with MAE of 1.63 Mg ha−1 and d -Stat of 0.9. Overall, implementing deficit irrigation in green bean and sweet corn fields could result in considerable water savings while achieving acceptable crop yield. The DSSAT model could be also a useful tool to simulate green bean and sweet corn responses to different irrigation scenarios and aid water management decisions under South Florida conditions. • Deficit irrigation conserves water while maintaining sweetcorn and green bean growth and yields. • DSSAT helps in assessing deficit irrigation impact and improving water productivity. • Integrating experiments and crop growth model enhances knowledge of deficit irrigation. [ABSTRACT FROM AUTHOR]

• A new visible NDVI (vNDVI) was developed using genetic algorithms. • It was evaluated in three different crops (citrus, grapes, and sugarcane). • The vNDVI accurately estimates NDVI values from RGB data. • It can provide a low-cost alternative for plant phenotyping. Several vegetation indices have been developed, with the normalized difference vegetation index (NDVI) been the most studied and commonly used. To generate an NDVI map, a relatively high-cost multispectral sensor is required; but currently, most UAVs are equipped with low-cost RGB cameras. For that reason, other indices that utilize RGB data have been developed to generate maps similar to NDVI and minimize the data acquisition cost, such as the triangular greenness index (TGI) and the visible atmospheric resistant index (VARI). However, several studies found that these indices cannot be recommended as reliable general-purpose crop health indicators. This study utilizes a genetic algorithm to develop a new visible index (visible NDVI; vNDVI) that estimates NDVI values of vegetation from uncalibrated RGB cameras mounted on UAVs (or other remote sensing platforms). Three experiments were conducted to create and validate the proposed index. First, the NDVI values generated from a multispectral camera were compared with the NDVI values generated by a hyperspectral camera. In the second experiment, the vNDVI formula was created using a genetic algorithm. The third experiment validates the proposed vNDVI, generated from two uncalibrated RGB cameras, in three different crops (citrus, grapes, and sugarcane). The proposed vNDVI proved to be highly accurate on estimating NDVI values by just using RGB cameras, with an overall mean percentage error of 6.89% and a mean average error of 0.052 in all three crops, providing a low-cost alternative for remote sensing and plant phenotyping. [ABSTRACT FROM AUTHOR]

Laurel wilt (Lw) is a very destructive disease and poses a serious threat to the commercial production of avocado in Florida, USA. External symptoms of Lw are similar to those that are caused by other diseases and disorders. A rapid technique to distinguish Lw infected avocado from healthy trees and trees with other abiotic stressors is presented in this paper. A novel method was developed to analyze data from hyperspectral data using finite difference approximation (FDA) and bivariate correlation (BC) to discriminate Lw, Nitrogen (N), and Iron (Fe) deficiencies from healthy avocado plants. Several combinatorial methods were used in preprocessing the data, such as standard normal transformation of data, smoothing of the data, and polynomial fit. The FDA technique was derived using a Taylor Polynomial finite difference approximation. This FDA accentuates inflection points in the spectrum. These, in turn, reveal variance in the data that can be used to identify spectral signature associated with healthy and diseased states. By statistical correlation using the bivariate correlation coefficient of these enhanced spectral patterns, an algorithm (FDA-BC) for distinguishing Lw avocado leaves from all other categories of healthy or mineral deficient avocado leaves is achieved with an overall accuracy of 100%. [ABSTRACT FROM AUTHOR]

• The use of Unmanned Aerial Vehicles (UAVs) and machine learning techniques for plant phenotyping has shown great potential for increasing the efficiency and accuracy of crop monitoring. • These technologies can provide high-resolution images and data that can be used to analyze plant traits and predict crop yield. • By automating the process of data collection and analysis, UAV-based imaging and machine learning techniques can reduce the time and labor required for traditional crop monitoring methods. • Furthermore, they can enable real-time decision making, which is critical for optimizing crop management strategies and achieving higher yields. The availability of timely and accurate information about plant conditions is critical for making informed decisions for maintaining or improving agricultural productivity. However, field data collection is often laborious and time-consuming, underscoring the need for accurate and efficient methods to monitor crop growth and development. Therefore, the objective of this study was to evaluate the effectiveness of combining unmanned aerial vehicle (UAV)-based imaging and machine learning (ML) techniques for monitoring sweet corn (Zea mays var. saccharata) height, biomass, and yield. The study was conducted at the Tropical Research and Education Center (TREC) during the winter (dry) season of 2020–2021 using 16 experimental plots planted with sweet corn. The treatments were set up in a completely randomized block design (RCBD) with four irrigation treatments of 25%, 50%, 75%, and 100% full irrigation with four replications each. Field data collection included plant height, fresh and dry biomass, and yield. In addition, UAV images were collected using the DJI Matrice 210 v2 UAV (SZ DJI Technology Co., Ltd., Shenzhen, China) equipped with a MicaSense RedEdge-MX multispectral sensor (MicaSense, Seattle, WA, USA). Image processing was done with Pix4Dmapper 4.7.5 (Pix4D S.A., Prilly, Switzerland). A crop surface model, representing estimated plant height (UAVH), was calculated based on pixel-to-pixel differences between digital surface and terrain models. A simple linear regression model was used to estimate sweet corn biomass and yield from UAV images estimated plant height (UAVH). In addition, two linear algorithms known as a linear model (LM) and Lasso and elastic-net regularized generalized linear model (GLMNET) and three non-linear ML algorithms including random forest (RF), support vector machine (SVM), and k-nearest neighbor (kNN) were used to predict plant height and biomass. These algorithms were chosen due to their reliable performance and ability to learn complex non-linear relationships. Eight vegetation indices with UAVH were also used to evaluate the ML models' performance to predict plant height and biomass. Results confirmed that UAV imaging could be effectively used to estimate plant height (d = 0.99, r2 = 0.99, and MAE = 5 cm). Estimated fresh and dry biomass from UAV imaging also had good agreements with measured data with r2 values of 0.75 and 0.70, respectively. A statistically significant linear correlation between measured fresh yield at harvest and UAVH was found with d, adjusted r2 , and MAE of 0.84, 0.66, and 67 g m−2, respectively. Evaluation of ML algorithms revealed that all models performed well for plant biomass estimation with d values between 0.88 and 0.99. However, the kNN and SVM outperformed all other models for biomass estimation. GMLNET performed better than other models for plant height estimation. Overall, results revealed that UAV imaging and ML models could be effectively used for monitoring plant phenotypic characteristics such as height, yield, and biomass. [ABSTRACT FROM AUTHOR]

A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle (UAV). In the laboratory, a hyperspectral (400–1000 nm) imaging system was utilized for the detection of citrus canker in several disease development stages (i.e., asymptomatic, early, and late symptoms) on Sugar Belle leaves and immature (green) fruit by using two classification methods: (i) radial basis function (RBF) and (ii) K nearest neighbor (KNN). The same imaging system mounted on an UAV was used to detect citrus canker on tree canopies in the orchard. The overall classification accuracy of the RBF was higher (94%, 96%, and 100%) than the KNN method (94%, 95%, and 96%) for detecting canker in leaves. Among the 31 studied vegetation indices, the water index (WI) and the Modified Chlorophyll Absorption in Reflectance Index (ARI and TCARI 1) more accurately detected canker in laboratory and in orchard conditions, respectively. Immature fruit was not a reliable tissue for early detection of canker. However, the proposed technique successfully distinguished the late stage canker-infected fruit with 92% classification accuracy. The UAV-based technique achieved 100% classification accuracy for identifying healthy and canker-infected trees. [ABSTRACT FROM AUTHOR]

• An automated system for monitoring ACP in groves was developed. • This low-cost technology can distinguish target insects from other insects and objects. • Neural networks were utilized for ACP detection and classification. • It develops a map to visualize the ACP numbers for each tree. • It can automate scouting procedures in citrus and to be extended to other crop insects. Specialty crop growers face challenges from numerous diseases and pests. For example, the Asian citrus psyllid (ACP) is a key pest of citrus due to its role as vector of huanglongbing (HLB) (greening disease). There is no known cure for HLB, but vector management is critical, both for slowing spread and attenuating symptoms in infected trees. Therefore, monitoring ACP population, as well as other pest populations, is an essential management component for timing and assessment of control actions. Manual crop scouting is often labor intensive and time consuming. In this work, an automated system was developed and evaluated utilizing machine vision and artificial intelligence to monitor ACP in groves. This system comprised a tapping mechanism to collect insects from the tree's branches and a board with a grid of cameras for image acquisition. A software was developed using two convolutional neural-networks to accurately detect and distinguish psyllids from other insects and debris fallen from the tree. A GPS was utilized to automatically record individual tree position to facilitate data assessment on large groves. A precision and recall of 80% and 95%, respectively, was obtained on detecting ACPs on a sample of 90 young citrus trees. The system proved a great potential to automate scouting procedures in citrus and to be extended to other crop insects. [ABSTRACT FROM AUTHOR]

Thermotherapy, as a non-chemical method to treat pests, has many advantages over the conventional chemical methods for treating some plant diseases. However, applying this method in the field, especially in orchards, can be difficult due to technical complications with regards to total coverage of large trees. In this work, a mobile thermotherapy system was designed and developed to provide heat treatment to individual citrus trees infected with Huanglongbing (HLB) disease. Based on previous studies, the system was designed to produce heat inside a canopy cover that covered a whole citrus tree, raising the internal temperature to 54 °C and treating the tree for 90 s. The system was able to increase the heat inside the canopy cover using different heat sources by either releasing steam or a combination of steam and hot water. A series of experiments were conducted to assess the uniformity of heat distribution within the tree canopy and the time to reach an appropriate temperature utilising two treatment types: Steam Treatment (ST) and Hot Water & Steam Treatment (HWST). Results indicated that the uniformity of heat is similar with both treatment types, while the average time for the entire treating process was 63% shorter when using HWST compared to ST. Furthermore, HWST reduced energy consumption by 52% compared to ST. The present study focuses on the technical evaluation of an thermotherapy system and does not evaluate the treatment efficiency and tree condition after treatment, which will be the subject of future experimental work. • A mobile thermotherapy technology was developed for citrus trees. • Steam Treatment (ST) and Hot water & Steam Treatment (HWST) were examined. • Heat distribution is similar in both treatments. • HWST reduced the treatment time by 63% and energy consumption by 52%. • Both treatment did not irreversibly harm trees. [ABSTRACT FROM AUTHOR]

Highlights • A remote sensing technique for Laurel Wilt detection in avocado was developed. • This low-cost technique can distinguish Laurel Wilt disease from other stressors. • Neural networks were utilizing for disease detection and classification. • Early disease detection was achieved utilizing multispectral imaging. Abstract Early and accurate disease detection is essential for implementing timely disease management practices. Current disease detection tactics, like visual detection through scouting, are labor intensive, expensive, requires a level of expertise in pest identification, and, may result in subjective disease identification. Diagnosis based on visual symptoms is often compromised by the inability to differentiate between similar symptoms caused by different biotic and abiotic factors. In this paper, an automated early disease detection technique for avocado trees is presented and evaluated. This remote sensing technique can detect an important avocado disease, the laurel wilt (Lw) disease, and differentiate it from healthy trees (H), trees infected by phytophthora root rot (Prr), and trees with iron (Fe) and nitrogen (N) deficiencies. Detection of Lw disease in avocado trees, in early stage, is very difficult, because it has similar symptoms with other stress factors such as nutrient deficiency, salt damage, phytophthora root rot, etc. The proposed disease detection procedure contains several steps including image acquisition, image pre-processing, image segmentation, feature extraction and classification. For image acquisition, two cameras were utilized and evaluated: (i) a Tetracamera (6 bands Tetracam) and (ii) a modified Canon camera (3 bands); and two classification methods were studied: (a) neural network multilayer perceptron (MLP), and (ii) K- nearest neighbors, to detect Lw in asymptomatic stage and in late (symptomatic) stage. Additionally, two segmentation methods, region of interest (OVROI) and polygon region of interest (PROI), were utilized. The MLP classification method with the Tetracam was able to successfully detect Lw with an accuracy of 99% in asymptomatic (early) stage. Hence, low-cost remote technique can be utilized to differentiate healthy and unhealthy plants. [ABSTRACT FROM AUTHOR]

Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU. • The learning to augment strategy was used for saffron fraud detection and grading. • Several AI-enhanced techniques were compared to improve classification accuracy. • Corrupted images were used to address the models' overfitting problem. • The LAII-v4 CNN achieved a classification accuracy of 99.5%. [ABSTRACT FROM AUTHOR]

• Pecan nut follows a simple sigmoid curve from fruit set until the harvest of the nut. • Gompertz model fit best for the embryo development whereas logistic model show high efficiency for shell. • Both logistic and gompertz model show high efficiency for the development of shuck. • Finding from research can guide in building an integrated growth model with three phenotypic traits (shuck, shell, and embryo). • Model can aid in orchard management practices like irrigation, thinning and pest management. Pecan is the only commercial tree nut native to the United States. The optimal times for orchard irrigation, pesticide sprays, and harvest are difficult to identify. Growth and development of the nut as a function of heat unit accumulation is a potential tool predicting different nut growth stages. Growth models utilizing heat unit accumulation are useful decision making tools for growers to fine tune inputs. The objective of this research was to compare different non-linear growth models and determine which best fits the growth and development of the pecan nut components i.e., embryo, shell, and shuck. The sampled nuts were collected in two different years (2019 and 2020). In 2019, three varieties of nuts were sampled from five different locations. In 2020, samples of two varieties were collected from one location. Separate models were fitted for (1) the 2019 data to capture the variation among different locations and varieties and (2) the 2019 and 2020 data within one location that also considered the random effect between years for the two cultivars. A comparison of the coefficient of determination (R2) and Akaike information criteria (AIC) among the fitted models, demonstrated the Gompertz model best fit the embryo development for both datasets. It also best described the shuck growth in the 2019 data. The Logistic model performed slightly better than Gompertz for modeling shell growth for both datasets and the shuck for the two-year data. In both years, slow initial growth of fruit was observed until the beginning of the water stage, followed by an increase in size until the gel stage through nut filling in the dough stage. Our findings suggest that irrigation is more crucial during the water stage of pecan, whereas thinning should be done at the late water stage prior to the nut filling stage. Similarly, pesticide use can be minimized by determining when the pecan shell lignifies and is not susceptible to piercing sucking insect mouthparts. These results can guide pecan growers in fine tuning irrigation, thinning, and pest management based on an integrated growth model of the three phenotypic traits, shuck, shell, and embryo, as a function of accumulated heat units. [ABSTRACT FROM AUTHOR]

Huanglongbing (HLB or citrus greening) is a disease caused by an insect-transmitted bacterial pathogen Candidatus Liberibacter asiaticus (CLas). Thermotherapy has been successfully used by others to reduce the population of CLas bacteria in HLB-affected citrus trees under greenhouse studies. Thermotherapy is the application of heat as a strategy to reduce the adverse economic impact of certain pests and diseases. CLas is a fastidious, non-cultivable organism. The high variance in CLas titers in canopy samples together with this lack of cultivability makes it impossible to use classical bacteriological techniques to measure the viability either before or after treatments. Therefore, we used the survival rates of a surrogate bacterium, Klebsiella oxytoca, in order to evaluate the effectiveness of a mobile thermotherapy delivery system developed for in-field treatment of HLB-affected trees. K. oxytoca is a Gram-negative, rod-shaped bacterium that was originally isolated from soil and has been used in the development of industrial applications related to ethanol fuel production. It served as a biologically-based sensor of temperature stress (biosensor) in this study. Thermocouples and biosensor packets (plastic cups with suspended small snap-top tubes) containing the K. oxytoca were attached to an HLB-affected citrus tree and their canopy locations mapped. The mobile thermotherapy treatment hood covered the canopy of the HLB-affected tree. Then, steam and hot water were injected through nozzles inside of the hood to increase the temperature of the tree canopy. A standard temperature–time combination of 54 °C for 90 s was chosen based on preliminary studies where heat treatment parameters caused a significant reduction in CLas populations without inflicting permanent damage to the tree. The survival ratio of the K. oxytoca in the biosensor packets was found to range from complete elimination to 5% with treatments of 250 s and a maximum temperature of 54 °C. [ABSTRACT FROM AUTHOR]

This paper investigates the influences of several limiting factors on the performance of ground penetrating radar (GPR) in accurately detecting huanglongbing (HLB)-infected citrus roots and determining their main structural characteristics. First, single-factor experiments were conducted to evaluate GPR performance. The factors that were evaluated were (i) root diameter; (ii) root moisture level; (iii) root depth; (iv) root spacing; (v) survey angle; and, (vi) soil moisture level. Second, two multi-factor field experiments were conducted to evaluate the performance of the GPR in complex orchard environments. The GPR generated a hyperbola in the radar profile upon root detection; the diameter of the root was successfully determined according to the width of the hyperbola when the roots were larger than 6 mm in diameter. The GPR also distinguished live from dead roots, a capability that is indispensable for studying the effects of soil-borne and other diseases on the citrus tree root system. The GPR can distinguish the roots only if their horizontal distance is greater than 10 cm and their vertical distance is greater than 5 cm if two or more roots are in proximity. GPR technology can be applied to determine the efficacy of advanced crop production strategies, especially under the pressures of disease and environmental stresses. [ABSTRACT FROM AUTHOR]