Orchard Conditions Research Articles

Detection, counting, and maturity assessment of blueberries in canopy images using YOLOv8 and YOLOv9

Blueberry growers currently rely on manual or subjective approaches to harvest decision-making and yield estimation. Canopy image-based, pre-harvest blueberry detection provides a promising means for automated harvest maturity assessment, fruit counting, and yield estimation to optimize crop management and reduce labor costs. Data-driven deep-learning-based object detectors, especially the YOLO (You Only Look Once) series models, have emerged as a powerful tool for a diversity of computer vision tasks in agriculture. However, the detection of such small, densely populated objects as blueberries in unstructured scenes can pose great challenges, especially given the lack of dedicated datasets for model development. This study, therefore, presents the first publicly available dataset of blueberry canopy images with 17,955 ripe and unripe blueberries, which were captured in diverse orchard conditions, and performance evaluation of YOLOv8l (large) and YOLOv9-c (compact) with comparable complexity for blueberry detection and whereby fruit counting and ripe fruit percentage estimation. At the input image resolution of 2560 × 2560 pixels, both models performed similarly in terms of detection accuracy with around 91 % mAP@50, except that YOLOv9-c was far more time-consuming to train. Trained with the input of higher resolution images of 3520 × 3520 pixels, YOLOv8l achieved an overall mAP@50 of nearly 93 %, and an RMSEs (root-mean-square errors) of 10.4 in fruit counting and 3.62 % in estimating the percentage of ripe fruit of each image. Both the blueberry dataset11https://zenodo.org/records/14002517 and software programs22https://github.com/vicdxxx/BlueberryDetectionAndCounting of this study have been or will be made publicly available, which are expected to be useful for promoting greater community efforts for further algorithm development.

Effects of Water, Fertilizer and Heat Coupling on Soil Hydrothermal Conditions and Yield and Quality of Annona squamosa

Seasonal drought and summer soil high-temperature stress in Southern China often lead to decreased yield and quality of Annona squamosa. It is important to explore reasonable and effective water and fertilizer management measures as well as cover measures to improve the soil hydrothermal conditions in orchards to realize the increase in yield and quality of Annona squamosa. This study involved a two-year (2022–2023) field experiment in Yun County, Lincang City, Yunnan Province, using three factors and a three-level orthogonal test, resulting in nine different experimental treatments for water, fertilizer and heat. The three irrigation levels were W1 (soil moisture content of 55% of field moisture capacity), W2 (soil moisture content of 75% of field moisture capacity) and W3 (soil moisture content of 85% of field moisture capacity). The three fertilizer levels were F1 (1666 kg·hm−2), F2 (2083 kg·hm−2) and F3 (2500 kg·hm−2), and the three cover methods were A1 (no cover), A2 (fresh grass cover) and A3 (straw cover). The effects of these treatments on soil hydrothermal conditions, growth indices and fruit yield and quality of Annona s1uamosa were systematically monitored and analyzed, and the relationships between these treatments and yield and quality was analyzed based on a Mantel test. The results showed that T5 (W2F2A3) had the highest average soil moisture content over two years, followed by T7 (W3F1A3). The T7 (W3F1A3) treatment effectively reduced soil temperature by 5 °C compared to T1 (W1F1A1). T5 (W2F2A3) had the highest average yield over two years, with an increase of 33.99% compared to T1 (W1F1A1). Additionally, T5 (W2F2A3) has the highest average soluble solids, soluble sugars and vitamin C content over two years, with increases of 28.13%, 13.36% and 4.86%, respectively, compared to T1 (W1F1A1). A Pearson correlation analysis showed that there was a significant correlation between Annona squamosa growth and soil moisture content and soil temperature, and the Mantel test showed that soil hydrothermal conditions had significant influence on the growth and yield. T5 (W2F2A3) has the highest comprehensive benefit in promoting growth, increasing yield and improving quality for the plant. The effects of different irrigation quantities, fertilizer amounts and different cover measures on the coupling interaction for soil hydrothermal status in the root zone, growth, yield and quality of Annona squamosa were investigated, providing reliable theoretical support for the scientific planting model of Annona squamosa in the low-heat river valley of Yunnan Province.

Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments

Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational information for various automated or robotic tasks such as selective harvesting and precision pruning. This study compares the one-stage YOLOv8 and the two-stage Mask R-CNN machine learning models for instance segmentation under varying orchard conditions across two datasets. Dataset 1, collected in dormant season, includes images of dormant apple trees, which were used to train multi-object segmentation models delineating tree branches and trunks. Dataset 2, collected in the early growing season, includes images of apple tree canopies with green foliage and immature (green) apples (also called fruitlet), which were used to train single-object segmentation models delineating only immature green apples. The results showed that YOLOv8 performed better than Mask R-CNN, achieving good precision and near-perfect recall across both datasets at a confidence threshold of 0.5. Specifically, for Dataset 1, YOLOv8 achieved a precision of 0.90 and a recall of 0.95 for all classes. In comparison, Mask R-CNN demonstrated a precision of 0.81 and a recall of 0.81 for the same dataset. With Dataset 2, YOLOv8 achieved a precision of 0.93 and a recall of 0.97. Mask R-CNN, in this single-class scenario, achieved a precision of 0.85 and a recall of 0.88. Additionally, the inference times for YOLOv8 were 10.9 ms for multi-class segmentation (Dataset 1) and 7.8 ms for single-class segmentation (Dataset 2), compared to 15.6 ms and 12.8 ms achieved by Mask R-CNN's, respectively. These findings show YOLOv8's superior accuracy and efficiency in machine learning applications compared to two-stage models, specifically Mask-R-CNN, which suggests its suitability in developing smart and automated orchard operations, particularly when real-time applications are necessary in such cases as robotic harvesting and robotic immature green fruit thinning.

Efficacy and Joint Action of Some Biological and Safe Agents Against the Small Land Snail, Helicella vestalis, under Laboratory and Citrus Orchard Conditions

Efficacy and Joint Action of Some Biological and Safe Agents Against the Small Land Snail, Helicella vestalis, under Laboratory and Citrus Orchard Conditions

Improving real-time apple fruit detection: Multi-modal data and depth fusion with non-targeted background removal

In automated fruit detection, RGB-Depth (RGB-D) images aid the detection model with additional depth information to enhance detection accuracy. However, outdoor depth images are usually of low quality, which limits the quality of depth data. In this study, an approach/technique for real-time apple fruit detection in a high-density orchard environment by using multi-modal data is presented. Non-targeted background removal using the depth fusion (NBR-DF) method was developed to reduce the high noise condition of depth images. The noise occurred due to the uncontrolled lighting condition and holes with incomplete depth information in the depth images. NBR-DF technique follows three primary steps: pre-processing of depth images (point cloud generation), target object extraction, and background removal. The NBR-DF method serves as a pipeline to pre-process multi-modal data to enhance features of depth images by filling holes to eliminate noise generated by depth holes. Further, the NBR-DF implemented with the YOLOv5 enhances the detection accuracy in dense orchard conditions by using multi-modal information as input. An attention-based depth fusion module that adaptively fuses the multi-modal features was developed. The integration of the depth-attention matrix involved pooling operations and sigmoid normalization, both of which are efficient methods for summarizing and normalizing depth information. The fusion module improves the identification of multiscale objects and strengthens the network's resistance to noise. The network then detects the fruit position using multiscale information from the RGB-D images in highly complex orchard environments. The detection results were compared and validated with other methods using different input modals and fusion strategies. The results showed that the detection accuracy using the NBR-DF approach achieved an average precision rate of 0.964 in real time. The performance comparison with other state-of-the-art methods and the model generalization study also establish that the present advanced depth-fusion attention mechanism and effective preprocessing steps in NBR-DF-YOLOv5 significantly surpass those in performance. In conclusion, the developed NBR-DF technique showed the potential to improve real-time apple fruit detection using multi-modal information.

A novel jujube tree trunk and branch salient object detection method for catch-and-shake robotic visual perception

Visual perception has become a prerequisite for automated jujube harvesting robot operations under complex orchard conditions. Catch-and-Shake harvesting, as the most efficient and common harvesting method, has widely been applied on various manually operated harvesters to complete large-area jujube fruit harvesting. However, the main factors restricting the development of existing harvesters are labor shortage, high labor cost, and low operating efficiency. To address the issues, we designed a catch-and-shake harvesting robot for jujube tree trunks and branches visual perception that can provide a barrier-free catch-and-shake operation area and guide the manipulator to reach the area to complete the harvesting operation. Meanwhile, a visual perception system including tree trunks and branches detection, skeleton extraction, catch-and-shake area confirmation was presented to guide robot intelligent operations. In the visual perception system, a novel salientobjectdetectionmodel called feature intersection and fusion Transformer (FIT-Transformer) network was proposed to split branches and background to provide reference for determining safe catch-and-shake areas. Moreover, we designed a diverse feature aggregation (DFA) and an attention feature fusion module (AFFM) to strengthen feature learning capabilities and obtain robust perception models. Comparative experimental results showed that our proposed FIT-Transformer model outperformed 12 state-of-the-art (SOTA) algorithms including C2FNet, RAS, BASNet, U2Net, SCRNet, PiCANet, EDRNet, EGNet, ICONR, VST, TransSOD and ABiU_Net. Specifically, the segmentation accuracy of jujube tree trunks and branches using our method showed the satisfactory result on five evaluation indexes under natural environment (the EM, SM, WF, FM and MAE reached 0.9713, 0.8991, 0.8854, 0.8905, and 0.0302, respectively). Field experiments also proved that our method could meet the requirements of operational accuracy and real-time operations.

Orchard Mapping with Deep Learning Semantic Segmentation

Abstract: The goal of orchard mapping with deep learning semantic segmentation is to automatic detection and localization of the orchard tree canopy under varied situations like multiple seasons, various tree ages, and varying levels of weed covering. The accuracy of segmentation depends on many factors like season, canopy size, and presence of weeds, background soil condition, and untreated soil. The proposed research have several combinations of training & test data based on orchard conditions The proposed research work contains deep learning convolutional neural network variant. The algorithm U- net network will be used. This helps to automatic detect and localize the tree canopy size using UAV images. Image segmentation will help all farmers by automatically segmenting tree canopies from aerial images for the development of integrated tools. After measurement of these parameters, tree canopy volume can be detected of targeted tree

Naturally Occurring Yeasts Associated with Thaumatotibia leucotreta Can Enhance the Efficacy of the Cryptophlebia Leucotreta Granulovirus.

Yeasts associated with lepidopteran pests have been shown to play a role in their survival, development, and oviposition preference. It has been demonstrated that combining these yeasts with existing biological control agents can enhance their efficacy. The tortricid Thaumatotibia leucotreta is a phytosanitary pest in the South African citrus industry, with the baculovirus Cryptophlebia leucotreta granulovirus (CrleGV) being one of the components that can control this pest. Several yeast species were shown to be associated with T. leucotreta larvae, which affected their behaviour and development. A series of detached fruit bioassays were performed to determine whether the combination of yeast with CrleGV enhances its efficacy. These assays included determining the optimal yeast/virus ratio, testing all isolated yeast species in combination with CrleGV, and further improving yeast/virus formulation by adding an adjuvant. The optimal yeast concentration to use alongside CrleGV was determined to be 106 cells·mL-1. Pichia kluyveri, P. kudriavzevii, Kluyveromyces marxianus, and Saccharomyces cerevisiae in combination with CrleGV reduced larval survival compared to CrleGV alone. The addition of molasses and BREAK-THRU® S 240 to P. kudriavzevii and S. cerevisiae in combination with CrleGV did not notably improve their effectiveness; however, there was an observed decrease in larval survival. In future studies, field trials will be conducted with combinations of CrleGV and P. kudriavzevii or S. cerevisiae to investigate whether these laboratory findings can be replicated in orchard conditions.

Quantification and identification of damage caused by pests and fungi in dried figs from orchards with different levels of agronomic management in the main production areas of extremadura (SW Spain)

This work aimed to evaluate the effect of agronomic management and water regime on the number of lesions, levels of insect infestation and microbiological quality of dried figs in Extremadura. Dried fig samples from 18 orchards were collected. The results showed that birds were the primary pests, causing damage to dried figs, followed by insects and fungi. The effects of orchard management were more pronounced under irrigated conditions, with the percentage of undamaged dried figs and the number of insect-free fruits rising significantly with increasing management. Under rainfed conditions, the level of orchard management did not significantly influence damage. In addition, insects were detected in both damaged and undamaged dry figs. Cadra figulilella, Carpophilus hemipterus and Ceratitis capitata were the most common species. Regarding mycobiota, orchard conditions did not significantly affect fungi counts, but they did influence species composition. Aspergillus spp. were predominant under all conditions, followed by Alternaria spp. under irrigated conditions. This work provides relevant information on the different biotic agents that affect dried figs, showing that a higher level of management under irrigated conditions reduces pest incidence. Such knowledge is essential for designing control methods to obtain higher quality fruits.

Edge-based wireless imaging system for continuous monitoring of insect pests in a remote outdoor mango orchard

Mango production is a prominent tropical fruit industry worldwide. However, outdoor mango cultivation is susceptible to crop damage caused by insect pests and harsh environmental conditions. Integrated pest management (IPM) has emerged as a proposed solution to this problem. IPM utilizes data-driven and environmentally-friendly methods to suppress insect pest populations. Nevertheless, the collection of insect pest population data remains a laborious process, necessitating automation. This paper presents an image-based monitoring system to automatically record insect pest populations and environmental conditions in mango orchards. The system comprises solar-powered sensor nodes capable of periodically acquiring and analyzing sticky paper trap images. A modular deep learning-based algorithm was developed to detect and classify insect pests into seven classes, including major insect pests of mango such as thrips, mango leafhopper, and oriental fruit fly, with an average classification F1-score of 0.96. Unlike other insect counting algorithms, the algorithm reliably classifies insect pests according to different taxonomic levels even in non-laboratory environments. The monitoring system was tested and deployed in a remote mango orchard for over two years. The collected spatiotemporal information was analyzed to demonstrate the benefits of using the proposed system and recommend new IPM strategies. Temporal data analysis revealed a significant decrease in the count of selected insect pests after using the system, enabling identification of insect hotspots through statistical methods. This work presents a breakthrough in hardware and software solutions for developing smarter insect pest monitoring systems, leading to better IPM strategies.

Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management

Advanced sensing technologies and deep learning models are needed for automatic recognition of pathogens to protect trees in orchards. This study developed a fire blight disease detection and infected area segmentation system using image processing and deep learning approaches to automate the detection process in a complex apple orchard environment for site-specific management. Two types of images were acquired: multispectral images from an unmanned aerial vehicle (UAV) using a multispectral camera and red–greenblue (RGB) images from the ground using two different cameras. Multispectral images were preprocessed and used for image feature analysis by calculating vegetation indices, including excessive blue (ExB), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), modified ratio vegetation index (RVI), and triangular blueness index (TBI). Vegetation indices were calculated from a total of 60 multispectral images (30 heathy and 30 fire blight infected). Results showed that RVI was most sensitive to fire blight infection among the six indices. A support vector machine model was used to classify unhealthy tree canopies. A Mask Region-Convolutional Neural Network (Mask R-CNN) based deep learning model was developed from RGB infected images. A total of 880 images were used for training, and 220 images were used for validation. Another 110 images were used for testing the trained Mask R-CNN model. A precision of 92.8 % and recall of 91.2 % were obtained by detecting the infected canopies using a ResNet-101 backbone and intersection over union (IoU) threshold of 0.7. The high precision demonstrates the effectiveness of Mask R-CNN for the identification and segmentation of fire blight infection in images taken in complex orchard conditions. These results prove the potential of this non-invasive sensing method in detecting disease in commercial fruit production for site-specific infected canopies removing.

Design and Test of an Automatic Navigation Fruit-Picking Platform

With the development in agricultural mechanization and information technology, orchard agricultural machinery is also constantly pursuing the goal of intelligence and efficiency. Fruit picking is the most labor-intensive part of the orchard harvesting process. In order to resolve the problems of high labor intensity, low picking efficiency, and labor shortage when harvesting dwarf high-density orchards, an automatic navigation fruit-picking platform with voice control was developed in the present study. First, the platform utilized a voice-controlled high-level extendable working platform and a fruit-box-lifting device to adapt to varying orchard planting row spacing and enable convenient fruit box loading and unloading. Second, an automatic navigation system, which employed China’s Beidou navigation satellite system to acquire positional data and the Stanley algorithm for path-tracking control, was implemented. Third, the fruit-picking platform with automatic navigation system was fabricated and assembled, its outer wheel steering was measured to have a maximum angle of 30.3° and an average minimum turning radius of 4.5 m, meeting the turning radius requirements under orchard conditions. Finally, automatic navigation tests of the developed platform were performed in the orchard conditions. The results indicated that the platform could maintain a straight-line path with a maximum lateral deviation of 101.5 mm and a maximum absolute average deviation of 44.1 mm at 0.4 m/s. Under the U-shaped paths, the measured maximum lateral deviation was 148.6 mm and the maximum absolute average deviation was 57.2 mm. The navigation accuracy was sufficient to meet the requirements for the harvesting platform’s routine operation in the orchards.

Internal Flesh Browning in Apple and Its Predisposing Factors—A Review

This review article is focused on internal flesh browning (IFB)-related physiological disorders affecting apple (Malus domestica Borkh.) fruit. The expression of different physiological and metabolic IFB-related disorders during post-harvest storage are investigated along with the pre-harvest factors contributing to development. The effectiveness of commercially available pre-harvest technologies for preventing IFB-related disorders are also examined. Internal flesh browning-related disorders are erratic and devastating disorders that result in post-harvest deterioration of fruit quality in apples. Internal flesh browning-related disorders can result in severe economic losses to the apple industry through reduced consumer trust and market acceptability of susceptible cultivars. There are several IFB-related disorders and incidence can range from 0 to 100% of a crop, with severity ranging from no brown flesh to browning of the entire fruit flesh. While IFB-related disorders are found in several apple cultivars, some cultivars are more prone than others. The development of IFB-related disorders involve complex mechanisms depending upon the different types and causes, or factors involved in loss of structural integrity and functional stability of the cell membranes and cell components. Membrane disruption followed by enzymatic oxidation of fruit phenolic compounds by polyphenol oxidases and the production of brown polymers is considered to be the general underlying mechanism causing the browning of flesh tissue. It can be observed in different patterns based on the injured portion of the fruit flesh and the cause of membrane disruption. Three broad categories of IFB-related disorders, including chilling injury, internal CO2 injury, and senescent-related browning disorders, are discussed along with their sub-types. The development of IFB-related disorders can be influenced by both pre-harvest factors and post-harvest conditions and their interactions. Although commonly associated with storage, IFB can also be found immediately after harvest and sometimes in unharvested fruit prior to full maturity. As pre-harvest conditions are a strong contributor to IFB-related disorders, the influence of several pre-harvest orchard conditions, including fruit size, crop load, maturity at harvest, cultivar, climatic conditions, seasonal temperatures, growing degree days, and major mineral nutrients, such as nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) are reported. Although there are contradictory findings in the studies reported, in general, factors such as larger fruit size, light crop load and delayed harvesting, along with cool temperatures after bloom and warmer temperatures before harvest, increase the risk of IFB-related disorders. In relation to fruit mineral concentrations, high N and low Ca have been associated with increasing IFB, while there is conflicting evidence in relation to the impact of both P and K. This review also examines the effectiveness of commercial pre-harvest technologies such as 1-methylcyclopropene, aminoethoxyvinylglycine and diphenylamine in the prevention of IFB-related disorders, but none of these technologies were found promising due to varied and contradictory results.

Evaluation of the Antimicrobial Potential and Characterization of Novel T7-Like Erwinia Bacteriophages

The recent outbreak of blight in pome fruit plants has been a major concern as there are two indistinguishable Erwinia species, Erwinia amylovora and E. pyrifoliae, which cause blight in South Korea. Although there is a strict management protocol consisting of antibiotic-based prevention, the area and the number of cases of outbreaks have increased. In this study, we isolated four bacteriophages, pEp_SNUABM_03, 04, 11, and 12, that infect both E. amylovora and E. pyrifoliae and evaluated their potential as antimicrobial agents for administration against Erwinia-originated blight in South Korea. Morphological analysis revealed that all phages had podovirus-like capsids. The phage cocktail showed a broad spectrum of infectivity, infecting 98.91% of E. amylovora and 100% of E. pyrifoliae strains. The antibacterial effect was observed after long-term cocktail treatment against E. amylovora, whereas it was observed for both short- and long-term treatments against E. pyrifoliae. Genomic analysis verified that the phages did not encode harmful genes such as antibiotic resistance or virulence genes. All phages were stable under general orchard conditions. Collectively, we provided basic data on the potential of phages as biocontrol agents that target both E. amylovora and E. pyrifoliae.

Fire Hazard: Undesirable Ecosystem Function of Orchard Vegetation

Fires will become an increasingly frequent perturbation even under the conditions of the mild climate zone and will interfere with the agricultural landscape. Fire is a natural phenomenon, and depending on ecosystems, vegetation may develop and contribute to the occurrence and spread of fire. Vegetation of the sour cherry orchard located in the climatically dry conditions of the South Moravian Region, Czech Republic (CR), was evaluated. Vegetation assessment was performed using phytocenological relevé. In each variant, 10 relevé were recorded. Coverage of the found species was estimated directly in percentages. Moreover, the maximum height in the stand was measured for each type of plant. Biomass of individual plant species was calculated, using the biomass index (IB) equation. The IB values of individual plant species in the treatments were processed by employing a multidimensional analysis of the ecological data. Different vegetation management practices in an orchard change the species diversity of the vegetation and thus the fire hazards in the orchard conditions. Grassy interrow has the most grass biomass during the entire vegetation season, and therefore represents the greatest hazard and spread of fire. The most important grasses include Arrhenatherum elatius, Dactylis glomerata, Festuca pratensis, Lolium perenne, and Poa pratensis. On the contrary, bare soil conditions in the interrow are most suitable for annual species, and this is the place with the highest changes in the number of species during the growing season. Biomass of the orchard vegetation combined with dry and warm weather increases the fire hazard. Annual and perennial grasses have very good potential for the production of biomass, which increases the hazard of fire. The nature of the vegetation in the orchards has the potential for the actual start of a fire and its subsequent spread, however, under other environmental conditions. During hot and dry weather, dead biomass may accumulate resulting in increasing the hazard of large wildfires. Varied orchard management practices lead to a higher diversity of vegetation and make orchards, islands of biodiversity in the agricultural landscape.

Evaluating Soil Nutrient Status of Mandarin Orchards across Varied Altitudes in Gorkha, Nepal

Mandarin, a prominent crop in Nepal, exhibits remarkable adaptability to different altitudes, resulting in diverse soil and nutrient conditions in orchards. Understanding these variations is crucial for optimizing orchard management practices and ensuring sustainable fruit production. This research endeavor sought to assess the nutritional profile of the soil in mandarin positioned at diverse elevations in Gorkha district, Nepal. The orchards were classified into five elevation groups: 800 masl, 900 masl, 1000 masl, 1100 masl, and 1200 masl. Using randomized complete block design (RCBD), each altitude contained ten composite soil samples, and was then subjected to chemical analysis to assess their properties. The results showed that soil pH increased with altitude, with the highest pH (6.66±0.045) and soil organic matter (OM) (5.88±0.12%) at 1200 masl and the lowest pH (5.5±0.045) and OM (1.92±0.12%) at 800 masl. Soil acidity was observed 5.79±0.045 at 900 masl and 5.5±0.045 at 800 masl, while all other altitudes had neutral soils. Nitrogen content followed a similar trend, with the highest at 1200 masl (0.32±0.007%) and the lowest at 800 masl (0.10±0.007%). Phosphorus and potassium showed no significant variation with altitude. Available phosphorus was the highest at 1000 masl (72.93±2.02 kg ha-1) and the lowest at 800 masl (51.61±2.02 kg ha-1). Maximum available potassium (365.34±6.84 kg ha-1) was observed at 1100 masl, while the minimum (337.63±6.84 kg ha-1) was recorded at 800 masl. In 18% of the samples, nitrogen exhibited the lowest concentration, while phosphorus was limiting in 12% of the samples. Potassium was not found to be a limiting nutrient in any of the samples. Variations in soil pH, OM, and nutrient content call for altitude-specific nutrient management to optimize mandarin production. Further studies are required in diverse ecology to characterize nutrient requirements and enhance sustainable mandarin cultivation practices.

Production and marketing of Tuber floridanum – ecology and gastronomic value of a recently described truffle species

Tuber floridanum is the first truffle species reported in Brazil and little is known about its nutritional properties and ecology. Therefore, this study aimed to (1) determine T. floridanum aroma and nutritional properties and (2) analyze cultivation and fruiting conditions, including potential crop pests and market value. Fruiting bodies of T. floridanum showed a slightly ellipsoid shape and smooth to shallow groovy surface. The mass of ascocarps ranged from 0.13 g to 15.95 g, and the truffle was composed of 71 % carbohydrates, 19 % protein, 5 % ash, and 5 % of fat. Octan-3-one, 1-methoxy-3-methyl-butane, and 1-methoxy-2-methyl-butane determined T. floridanum characteristic aroma. Soil properties were also specific for this truffle, differing from optimal soils where commercial truffles are produced. In addition, the occurrence of earwigs (Anisolabididae, Anisolabidinae) feeding on truffles (26 % of all ascocarps) in pecan orchards may reduce truffle quality and cause financial losses. T. floridanum aroma has a unique composition, completely different from any commercial and non-commercial truffle species analyzed so far. Soil conditions in pecan orchards were also specific for truffle fruiting. Further studies should elucidate other ecological conditions for the co-production of T. floridanum with pecan .

Insect netting: effect of mesh size and shape on exclusion of some fruit pests and natural enemies under laboratory and orchard conditions

To improve exclusion systems for fruit trees, insect nets of various types were evaluated for their permeability to different beneficial and pest species, under laboratory and field conditions. Pests studied were the apple maggot, Rhagoletis pomonella (Diptera: Tephritidae) and the spotted wing drosophila, Drosophila suzukii (Diptera: Drosophilidae). Beneficials were Aphidoletes aphidimyza (Diptera: Cecidomyiidae), Aphidius matricariae (Hymenoptera: Braconidae) and Aphelinus abdominalis (Hymenoptera: Aphelinidae). Mesh nets with five different apertures (square, rectangle, triangle, rhombus and hexagon) and six different sizes (from 0.4 to 2.8 mm) were 3D-printed from strands of polylactic acid and tested in the laboratory along with two commercially available nets made of polyethylene. The physical and behavioral characteristics of the six studied species affected their ability to cross the nets. For an equal size (open area), the intrusion rate was generally greater through the square- and/or hexagonal-shaped meshes. Rectangular-shaped apertures totally excluded the apple maggot in both laboratory and field trials, provided their shortest side did not exceed 1.9 mm. For the spotted wing drosophila, a maximum of 1.0 mm was similarly required for exclusion in the laboratory. The shape factor (length/width ratio) of the apertures appeared to affect net selectivity. Field trials confirmed that more aphid predators and leafroller parasitoids colonized trees covered with larger mesh nets (2.3 × 3.4 mm), while still excluding the apple maggot. Thus, for a similar aperture size (area), an elongated rectangular-shaped mesh appears to facilitate access for beneficials, while continuing to provide effective protection against apple pests.

Screening of Venturia inaequalis virulence and resistance of Malus genotypes to apple scab using in vitro methodology

Abstract The apple scab induced by Venturia inaequalis is an economically significant disease of apples worldwide and is predominantly controlled by multiple fungicide applications. Therefore, resistant apple cultivation is important for long-standing disease control. The knowledge about cultivar resistance is mainly founded on their evaluation in orchard conditions and testing in a greenhouse that is laborious and requires a large space. This study evaluated apple cultivar resistance and the virulence of V. inaequalis strains by inoculating detached leaves and immature fruits. Nine V. inaequalis strains originating from different apple-growing regions and host genotypes were tested on eight apple genotypes. Microscopic and macroscopic symptom development and host tissue reactions were monitored during the experiments. The tested V. inaequalis strains and cultivars showed different levels of virulence and resistance, respectively. Cultivar ‘Lobo’ was scored as partially susceptible, with almost all strains tested. The incompatible interaction with or without host tissue reactions was observed only on the cultivar ‘Priscilla’ with all the strains and field populations tested. The results of this study using detached leaves and immature fruits were concordant with the data obtained in the cultivar evaluation trials in the orchards.

Early detection of graft-incompatibility in hawthorn (Crataegus azarolus L.) trees on apple, pear, and quince rootstocks

Aim of study: This study was conducted to determine the usability of some clonal rootstocks of apple (MM 106 and MM 111), pear (Fox 11), and quince (Quince A) for hawthorn trees propagation. Area of study: Fruit Research Institute, Isparta and Hatay Mustafa Kemal University, Hatay, Türkiye. Material and methods: ‘Sultan’ hawthorn cultivar was budded on the following clonal rootstocks: pear Fox 11, quince A, and apple MM 106 and MM 111. Plants of hawthorn seedlings (Crataegus azarolus L.) budded with ‘Sultan’ cv. were used as control. External visual diagnosis of the scion-rootstock graft combinations was performed by observing visual symptoms in budded trees growing in nursery conditions, as well as anatomic and histological investigations of the incompatibility in the laboratory. Main results: In this study, healthy scion development and callus tissue in the graft region were formed in the seedling rootstock as well as on Fox11 and Quince A clonal rootstocks. In contrast, MM 106 and MM 111 apple rootstocks showed incompatibility symptoms, with insufficient scion vigor and unstructured callus tissue development. Research highlights: ‘Tanslocated’ and ‘located’ graft incompatibility symptoms were observed in Sultan/MM 106 and Sultan/MM 111 combinations. Further studies are necessary to confirm the early good compatibility found in nursery conditions, testing the effect of those clonal rootstocks from different species (Fox11 and Quince A) on vigor, yield, and fruit quality traits in orchard conditions.