Orchards Research Articles

Comparison of ethanol-baited trap designs for ambrosia beetles in orchards in the eastern United States.

Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) are among the most devastating pests of orchards, nurseries, and forests. Improving trap design and ethanol lures for capturing ambrosia beetles is necessary to develop effective monitoring and management strategies. In this 2-year study, we assessed 4 trap designs and 3 commercially formulated ethanol lures to refine trapping methods tailored for orchard environments in the eastern United States. Our investigation included orchards in 2 regions, Georgia (pecan orchards) and New York (apple orchards), targeting major ambrosia beetle (Coleoptera: Curculionidae) pest species such as Xylosandrus crassiusculus (Motschulsky), X. compactus (Eichhoff), X. germanus (Blandford), and Anisandrus maiche (Stark). Among the trap designs evaluated, clear sticky cards were most effective for capturing ambrosia beetles across orchard locations. Notably, in Georgia, sticky cards paired with specific low-release ethanol lures demonstrated enhanced capture of X. crassiusculus and X. compactus, 2 key ambrosia beetle pests found infesting young pecan trees. Similarly, in New York, sticky cards baited with low-release ethanol lures captured the highest rates of X. germanus and A. maiche, thus indicating its suitability for diverse ambrosia beetle populations. Overall, our study provides practical implications for tailoring trapping protocols to optimize ambrosia beetle management strategies in orchard settings.

The Factors Affecting Orchard Fruit Farming in Hau Giang Province, Vietnam

The Factors Affecting Orchard Fruit Farming in Hau Giang Province, Vietnam

Marketing channel choice and its determinants among small-scale oil palm fruit farmers in Akwa Ibom State, the southern region of Nigeria

Marketing channel choice and its determinants among small-scale oil palm fruit farmers in Akwa Ibom State, the southern region of Nigeria

Field evaluation of female- and male-targeted traps for Ceratitis capitata (Diptera: Tephritidae).

Mediterranean fruit fly (Medfly) Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) is a globally significant economic pest for which lure based trapping can be used to monitor established populations and for surveillance. Either female- or male-targeted traps can be used; however, recommendations on which to apply are inconsistent and many programs rely on male-targeted traps. Here, we compare the performance of male-targeted traps (Lynfield Trap with Trimedlure) and female-targeted traps (Biotrap Globe trap with the 3-component lure-TMA Plus) in apple orchards in south-west Western Australia over 2 years (September 2019 to September 2021). Male-targeted traps caught more Medflies overall than female-targeted traps, although the difference was minor. However, female-targeted traps were better at attracting Medfly early in the season when populations were small; and were more likely to capture at least one fly when their paired male-targeted trap caught none. Conversely, male-targeted traps were more likely to capture Medflies late in the season and were more likely to catch high numbers of Medflies. Consequently, female-targeted traps may be better at detecting Medfly early in the season, and male-targeted traps may be better at detecting Medfly abundance late in the season, at least in apple orchards. Our results suggest that either or both trap-types could be used for monitoring Medfly populations, with the optimal solution being dependent on the intended application.

Metal bioaccumulation in some Auchenorrhyncha (Hemiptera) species in apple orchards

Traffic is an important pollution factor causing environmental damages such as soil, water and atmospheric pollution, greenhouse effect, and climate change. Effects of traffic pollution on various organisms enlightened with various studies. In this study, it was aimed to examine the effects of traffic-based pollution on Auchenorrhyncha (Insecta: Hemiptera) species in apple orchards and their potential as biomonitor for heavy metal pollution. The Auchenorrhyncha specimens were collected from the apple orchards near the Amasya-Samsun motorway in Türkiye. The heavy metal concentrations were determined by ICP-OES. Five Auchenorrhyncha species were determined from three sites from each of three different distance. Empoasca decipiens specimens were collected only from 0 m while others found in all localities. Heavy metal concentrations in insect specimens tended to decrease with the increasing distance from the motorway. These differences were clearly indicated in Psammotettix provincialis and Phlepsius intricatus, which were found in all localities. Except for Ni, Fe and Mn for Phlepsius intricatus, all examined heavy metals significantly varied in both species. Results showed that heavy metals tended to accumulate in the body of Auchenorrhyncha specimens and because of this they may be evaluated as a biomonitor for heavy metal pollution.

Deciphering the mechanisms involved in reduced sensitivity to azoles and fengycin lipopeptide in Venturia inaequalis

Apple scab, caused by the hemibiotrophic fungus Venturia inaequalis, is currently the most common and damaging disease in apple orchards. Two strains of V. inaequalis (S755 and Rs552) with different sensitivities to azole fungicides and the bacterial metabolite fengycin were compared to determine the mechanisms responsible for these differences. Antifungal activity tests showed that Rs552 had reduced sensitivity to tebuconazole and tetraconazole, as well as to fengycin alone or in a binary mixture with other lipopeptides (iturin A, pumilacidin, lichenysin). S755 was highly sensitive to fengycin, whose activity was close to that of tebuconazole. Unlike fengycin, lipopeptides from the iturin family (mycosubtilin, iturin A) had similar activity on both strains, while those from the surfactin family (lichenysin, pumilacidin) were not active, except in binary mixtures with fengycin. The activity of lipopeptides varies according to their family and structure. Analyses to determine the difference in sensitivity to azoles (which target the CYP51 enzyme involved in the ergosterol biosynthesis pathway) showed that the reduced sensitivity in Rs552 is linked to (i) a constitutive increased expression of the Cyp51A gene caused by insertions in the upstream region and (ii) greater efflux by membrane pumps with the involvement of ABC transporters. Microscopic observations revealed that fengycin, known to interact with plasma membranes, induced morphological and cytological changes in cells from both strains. Sterol and phospholipid analyses showed a higher level of ergosta-7,22-dien-3-ol and a lower level of PI(C16:0/C18:1) in Rs552 compared with S755. These differences could therefore influence the composition of the plasma membrane and explain the differential sensitivity of the strains to fengycin. However, the similar antifungal activities of mycosubtilin and iturin A in the two strains indirectly indicate that sterols are probably not involved in the fengycin resistance mechanism. This leads to the conclusion that different mechanisms are responsible for the difference in susceptibility to azoles or fengycin in the strains studied.

Analysis of Consumers’ Perception of Insecticidal Preservatives and Its Residues in Cucumber and Tomato from Selected Markets in Port Harcourt

A market survey involving 100 fruit sellers/consumers who were randomly selected in Fruit garden, Rumuokoro, Mile 3 and Oil Mill markets was conducted to examine their awareness of chemical preservatives, residual and dietary effects on their body. The study utilizes primary data which was collected through the use of questionnaire from the respondents and personal observation by the researchers. A semi structured standardized questionnaire was administered to the respondents to gather information about their socio demographic characteristics, knowledge, and attitude of the traders/consumers on nutrition and food preservation. Only 150 copies of the questionnaire were distributed by the researcher with the help of researches assistance. Data were analyzed using descriptive statistics such as frequency distributions, percentages, mean score and correlation coefficient. In this survey, the seller/consumers above 35 years old consumes more of the fruits (35%) and those of 70kg weight recorded above 52%. Only 29% of respondents are aware that fruits bought are usually preserved with banned chemicals (gammalin 20, carbide etc.) by the traders. The hazard index recorded by tomato (1.2 – 1.3) and cucumber (1.8 – 2.2) was high in both markets under study. The consumption of tomato and cucumber is correlated positively with Weight (0.091) (0.019), Height (0.086) (0.178) and Body mass index (0.016) (0.043).

Fruit quality and productivity of yellow passion fruit grown under different irrigation depths

The yellow passion (Passiflora edulis) is a fruit tree belonging to the Passifloraceae family. Brazil is one of the world’s largest producers and consumers of the fruit. Irrigation is directly important for productivity and drip irrigation is the most appropriate method used by passion fruit farmers. Correct irrigation management directly influences the success of the crop. This work aimed to evaluate the effect of different irrigation depths on: fruit quality; physical-chemical parameters; and crop productivity. The experiment was carried out in the field, on private property. The design was in randomized blocks, with six treatments (irrigation depths) and four replications. The treatments were: T1 – Depth corresponding to the crop’s evapotranspiration; T2, T3, T4 and T5 – Depths corresponding to 33%, 80%, 133% and 200% of the depth applied by the farmer, respectively; and T6 – Depth applied by the farmer. The following variables were evaluated: plant height and stem diameter; physical-chemical characteristics of fruits and productivity. The different depths applied did not affect the quality of the passion fruit, but affected productivity, with the highest depth corresponding to the highest productivity and the smallest corresponding to the lowest.

Incidence and spatial pattern of viruses in commercial dragon fruit orchards in southern Minas Gerais, Brazil

Incidence and spatial pattern of viruses in commercial dragon fruit orchards in southern Minas Gerais, Brazil

Non-destructive detection of apple fungal infection based on VIS/NIR transmission spectroscopy

The study is dedicated to developing a precise and efficient detection method specifically for identifying infections in apples caused by fungi. This type of infection not only damages the quality and value of apples but also poses potential risks to human health. By integrating four machine learning algorithms (LDA, KNN, LSSVM, and RF) into a Soft Voting model, as well as combining VIS/NIR transmission spectroscopy with CARS and SPA feature wavelength screening methods, this study significantly improves the accuracy of detecting fungal infections in apples. The research results demonstrate that the Soft Voting model achieves 93.33 % TPR, 100 % TNR, 100 % precision, and 98.75 % accuracy, which has far-reaching implications for safeguarding food safety, reducing economic losses, and enhancing the reputation of fruit farmers and merchants.

Utilization of Livestock Manure as Biogas and Liquid Organic Fertilizer (LOF) in Sumbermulyo Village, Pesanggaran, Banyuwangi

Biogas is a natural gas produced through anaerobic bacteria breakdown of organic materials. Organic materials can include livestock manure, domestic waste, and other organic waste that bacteria can decomposeunder anaerobic conditions. Sumbermulyo is one of the villages in the Pesanggaran sub-district of Banyuwangi Regency, where the majority of the population works as dragon fruit farmers and goat herders. The current phenomena in the area include the scarcity of subsidized urea fertilizer and Liquified Petroleum Gas (LPG). An alternative approach is to utilize goat manure as a source of methane gas, which can be used as a substitute for LPG. The objective of this community service project is to establish a biogas processing pilot project in the Sumbermulyo Village, Pesanggaran sub-district, which the community can implement. Additionally, it aims to analyze the total N, P, K content in the wet sludge from the digester as liquid organic fertilizer. The method used involves constructing a biogas digester on the land owned by one of the farmer groups and evaluating the gas production. The digester is built using a concrete model (fixed dome) in the shape of an airtight dome. The construction of the digester takes 14 days until it is ready for use. Up to the 10th day of use, thehighest pressure is obtained on day -10, with a pressure of 107,486.76 N/m2 or 1.06 atm. Chemical analysis of the liquid organic fertilizer shows that the total N, P, K content is as follows: 0.032%, 0.009%, 0.144% for the sample from the test digester, and 0.044%, 0.005%, and 0.195% for the sample from the reference digester

ECOLOGY OF THE NILGIRI LARGE BURROWING SPIDER HAPLOCLASTUS NILGIRINUS POCOCK, 1899 (ARANEAE, THERAPHOSIDAE) OF NILGIRIS, INDIA

The Nilgiri large burrowing spider, Haploclastus nilgirinus Pocock, 1899, an endemic tarantula in the Nilgiris Region of India, is critical for conservation yet poorly understood in terms of its ecology and habitat. This study addresses this gap by examining the nesting habits, tree-hole dynamics, and prey diversity of the species in Coonoor, The Nilgiris, specifically within tea plantations and fruit orchards. Observations focused on seven pear trees serving as nesting sites, with detailed measurements of nesting holes, including outer and inner circumference, depth, trunk base, ground-based height (gbh), and height from the ground. Data were collected over four consecutive months, both morning and evening. Insect diversity in the orchard was also assessed through counts and diversity indices, revealing variability among trees. The seventh tree had the highest spider population, with 13 spiders in 10 nests. Insect orders such as Blattodea, Orthoptera, Diptera, Hemiptera, and Lepidoptera were most prevalent around the seventh tree’s nests. Across all trees, a total of 25 spiders in 21 nests were documented, with variations in hole size and other tree measurements. This research provides crucial insights into the nesting habits, tree-hole dynamics, and prey diversity of this species, informing conservation efforts for this vulnerable species. As H. nilgirinus faces threats from habitat destruction, climate change, and illegal trade, understanding its ecological needs is vital for developing effective conservation strategies and sustaining ecosystem health.

Development of a Cross-Platform Mobile Application for Fruit Yield Estimation

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. The Fruit Harvest Helper seeks to simplify their process by detecting apples on images of apple trees. Once the number of apples is detected, a correlation can then be applied to this value to obtain a usable yield estimate for an apple tree. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. Borrowing ideas from the former iOS app, the new application was designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app uses a color ratio-based image-segmentation algorithm written in C++ to detect apples. This algorithm detects apples within apple tree images that farmers select for processing by using OpenCV functions and C++ code. The results of testing the algorithm on a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. The algorithm’s processing time was tested for Android and iOS, yielding an average performance of 1.16 s on Android and 0.14 s on iOS. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture. The app is nearing readiness for farmers to use for the purpose of yield monitoring and farm management within Pink Lady apple orchards.

Review of modern methods for optimizing apple mineral nutrition to increase yield and fruit preservation

Achieving sustainable fruit production and ensuring quality preservation are paramount in modern apple orchards. This review delves into contemporary methods designed to optimize mineral nutrition, aiming to bolster both yield and fruit preservation. Various strategies for enhancing apple orchard management are examined, with a focus on maximizing the efficacy of mineral fertilizers, maintaining balanced nutrition, and understanding the critical role of key nutrients such as nitrogen, phosphorus, potassium, calcium, and boron. The review underscores the importance of comprehending plant nutrient requirements across different growth stages and phenological phases to attain sustainable, high-quality fruit production. Furthermore, it emphasizes the necessity of continuous diagnostic assessments to tailor mineral nutrition practices to the specific needs of apple orchards, ensuring optimal performance and economic viability.

Assessing a multi-camera system to enhance fruit visibility for robotic harvesting in a V-trellised apple orchard

Accurate detection and localization of fruits within the canopy are crucial for various tasks, such as perception for robotic harvesters, vision-based yield estimation, and early disease detection. However, complex canopy structures render fruit detection challenging due to obstructions from leaves, branches, and other fruits. This study assesses the efficacy of utilizing multiple cameras at varying elevations and azimuthal angles to enhance fruit visibility. We used a V-trellised apple orchard as a case study, where four cameras were employed; the first was oriented normally to the fruit canopy, while three others maintained a fixed position relative to the first but with variable orientation. Adjusting elevation and azimuthal angles revealed that, in the region of interest of the canopy, a single camera whose optical axis was perpendicular to the canopy could detect up to 88.3% of the fruits detected by four cameras. Adding one more camera – for a total of two cameras – increased the detection rate up to 97.5% of the four-camera detection rate. Also, the experimental results showed that three cameras could detect between 96.0% to 99.7% of fruits compared to four cameras under the proposed camera configuration. Hence, the utility of adding a third or fourth camera would need to be considered carefully, given the added cost and complexity. The results of this study are a step toward exploring and optimizing multiple-camera perception systems for orchard operations, in particular, robotic harvesters.

FF3D: A Rapid and Accurate 3D Fruit Detector for Robotic Harvesting.

This study presents the Fast Fruit 3D Detector (FF3D), a novel framework that contains a 3D neural network for fruit detection and an anisotropic Gaussian-based next-best view estimator. The proposed one-stage 3D detector, which utilizes an end-to-end 3D detection network, shows superior accuracy and robustness compared to traditional 2D methods. The core of the FF3D is a 3D object detection network based on a 3D convolutional neural network (3D CNN) followed by an anisotropic Gaussian-based next-best view estimation module. The innovative architecture combines point cloud feature extraction and object detection tasks, achieving accurate real-time fruit localization. The model is trained on a large-scale 3D fruit dataset and contains data collected from an apple orchard. Additionally, the proposed next-best view estimator improves accuracy and lowers the collision risk for grasping. Thorough assessments on the test set and in a simulated environment validate the efficacy of our FF3D. The experimental results show an AP of 76.3%, an AR of 92.3%, and an average Euclidean distance error of less than 6.2 mm, highlighting the framework's potential to overcome challenges in orchard environments.

Edge compute algorithm enabled localized crop physiology sensing system for apple (Malus domestica Borkh.) crop water stress monitoring

Elevated air temperature (>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p < 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.

A shift towards softer pest management differently benefits earwig and spider communities in a multiyear experiment in apple orchards.

Environmentally-friendly crop protection practices are needed to enhance the sustainability of current agricultural systems. This is crucial in orchards which are extensively treated to impair various pests, at the expense of natural enemies. However, the effect of a shift towards softer pest management on the beneficial arthropod community is poorly documented. Earwigs (Dermaptera: Forficulidae) and spiders (Araneae) are relevant groups to assess such effects because they are highly sensitive to agricultural practices. They were monitored for 6 and 4 years, respectively, in apple orchards under three pest management regimes: Organic, Low-input and Conventional, with pest management being switched during the survey from a broad-spectrum insecticide schedule to mating disruption in the latter one, and more selective compounds in all orchards. The survey displayed that earwig abundance (mainly Forficula auricularia) that was initially very low in the Conventional orchard (annual mean 0.5-1.7 earwigs per shelter in the 2010-2012 period) increased to the same level as that of Low-input and Organic orchards (over 10 earwigs per shelter) in the same year that changes in pest management occurred. The epigeal and arboreal spider communities were not responsive, and no recovering was observed 4 years after change in practices. Predatory arthropod communities are differently affected over time by changes in pest management, most probably due to their biology (dispersion, reproduction rate, susceptibility to pesticides etc.). This outlines the importance of documenting the time required to recover after perturbations and build a natural enemy community to enhance pest control in a win-win perspective. © 2024 Society of Chemical Industry.

3D Camera and Single-Point Laser Sensor Integration for Apple Localization in Spindle-Type Orchard Systems.

Accurate localization of apples is the key factor that determines a successful harvesting cycle in the automation of apple harvesting for unmanned operations. In this regard, accurate depth sensing or positional information of apples is required for harvesting apples based on robotic systems, which is challenging in outdoor environments because of uneven light variations when using 3D cameras for the localization of apples. Therefore, this research attempted to overcome the effect of light variations for the 3D cameras during outdoor apple harvesting operations. Thus, integrated single-point laser sensors for the localization of apples using a state-of-the-art model, the EfficientDet object detection algorithm with an mAP@0.5 of 0.775 were used in this study. In the experiments, a RealSense D455f RGB-D camera was integrated with a single-point laser ranging sensor utilized to obtain precise apple localization coordinates for implementation in a harvesting robot. The single-point laser range sensor was attached to two servo motors capable of moving the center position of the detected apples based on the detection ID generated by the DeepSORT (online real-time tracking) algorithm. The experiments were conducted under indoor and outdoor conditions in a spindle-type apple orchard artificial architecture by mounting the combined sensor system behind a four-wheel tractor. The localization coordinates were compared between the RGB-D camera depth values and the combined sensor system under different light conditions. The results show that the root-mean-square error (RMSE) values of the RGB-D camera depth and integrated sensor mechanism varied from 3.91 to 8.36 cm and from 1.62 to 2.13 cm under 476~600 lx to 1023~1100 × 100 lx light conditions, respectively. The integrated sensor system can be used for an apple harvesting robotic manipulator with a positional accuracy of ±2 cm, except for some apples that were occluded due to leaves and branches. Further research will be carried out using changes in the position of the integrated system for recognition of the affected apples for harvesting operations.

Using a rainfall simulator thrust type to evaluate the efficacy of insecticides in a apple orchard

A rainfall simulator thrust type was evaluated as a novel tool for studying the efficacy of insecticides against pests in apple orchards, compared to traditional laboratory methods. The simulator was installed and calibrated to simulate rainfalls of 50 mm h-1 on a 7-year-old apple orchard. The results of Grapholita molesta (Lepidoptera: Tortricidae) control in the field differed from those obtained in laboratory. The control efficacy in the orchard was lower than that obtained in laboratory tests. Control percentages varied throughout the simulated rains, with increases at the beginning of rainfall followed by decreases. The use of the push-type rainfall simulator provides advantages to research experiments, as it allows for evaluations under field conditions at different developmental stages of plants in orchards under different management practices, with results closer to field reality than those currently generated by laboratory tests.