Fruit Position Research Articles

The effect of fruit position and bagging treatment on Gamboge disorder in mangosteen (Garcinia mangostana L.)

Gamboge disorder has a detrimental effect on mangosteen. The leakage of the gamboge may result from water availability. Thus, modifying the transpiration of the fruit by bagging might minimize the inappropriate leak of the gamboge produced by the fruit. The study’s objective was to understand the relationship between different fruit positions and bagging treatment on the gamboge disorder in mangosteen. The experiment was conducted on 10-years old trees by tagging young fruits, five replicates with two fruit positions (inside and outside), and bagging treatment (no bagging, transparent and black plastic bagging). The result showed that bagging the fruits inside the canopy does not affect fruit mass. However, bagging with transparent and black plastic of the fruits inside the canopy decreases fruit size. The fruit quality improves by black bagging on the inside canopy fruits. These findings demonstrate that bagging fruits outside the canopy lowers their quality. Black bags used to package fruits inside the canopy improve fruit quality. However, the treatment also causes more fruit to fall from the tree and decreases the nutrient content.

Winged Fruits of Friisifructus aligeri gen. et sp. nov. from the Late Cretaceous of Western North America

Premise of research. Fossil flowers and fruits with unresolved affinities among extant angiosperms may potentially represent extinct families or orders. The characterization and systematic assessments of such fossils will undoubtedly further our understanding of extinct angiosperm diversity, which is crucial for reconstructing the evolution of angiosperms in deep time. Here, we report two three-dimensionally preserved winged fruits from Campanian (∼82–80 Ma) deposits on Sucia Island, Washington State, that represent a new genus. We explore potential phylogenetic relationships of this genus relative to higher taxa across the angiosperm phylogeny.Methodology. The fossils were studied using microcomputed tomography (μCT) scanning and light microscopy. A three-dimensional reconstruction of one winged fruit was created from the μCT scans. The phyloscan method was used to assess the systematic positions of the winged fruits. Using the reconstruction, the fossil fruits were compared to extant angiosperms and extinct winged fruits to assess potential affinities.Pivotal results. The μCT scans and reconstruction of the winged fruit reveal a pentamerous perianth, five pairs of fin-wings, two whorls of persistent stamens, five semicircular nectary pads, an inferior unilocular ovary, and a style with three stylar branches. Phyloscan analyses recover the winged fruits within the rosid clade, but they cannot be assigned to any extant family or order within the group because of differing floral and fruit morphologies.Conclusions. The winged fruits are assigned to Friisifructus aligeri gen. et sp. nov. This new genus and species add to the diversity of winged fruits from the Cretaceous, which are relatively uncommon. Moreover, the unique morphology of Friisifructus indicates that this genus may belong to an extinct family of rosids.

Response of Soybean Yield and Certain Growth Parameters to Simulated Reproductive Structure Removal

Soybeans often encounter several in-season stressors that can alter retention of reproductive structures. To understand soybean response to structural losses through altered growth parameters—and, ultimately, yield—a field trial was established in Bixby, Oklahoma, in 2019 and 2020 and Perkins, Oklahoma, in 2019. Removal of reproductive structures occurred at full flower (R2), the beginning of pod development (R3), and the beginning of seed development (R5) and at three locations on the plant (top third (T), middle third (M), whole (W)). The impact of flower removal on yield at the R2 and R3 stages did not significantly differ from that in non-treated soybean. Pod removal as late as R5 from the upper fruiting positions (T) had a lesser impact on overall yield, with R5:T showing a reduction in seed number of 860 seeds plant−1, whereas R5:M was 1921 seeds plant−1 below the non-treated soybean. The middle portion of the mainstem was the location where the loss demonstrated was paramount at R5, as this region is a large sink and major contributor to yield. Late-season, stress-negating yield recovery, depending on the severity, may indicate that management practices should anticipate physiological limitations for stress, as well as the potential for relative yield recovery and yield improvement.

Evaluation of Chilli Varieties for Growth, Yield and Thrips Incidence in Central Telangana Zone

Aim: To know the performance of chilli varieties for growth, yield and its attributing characters, incidence of thrips and leaf curl index at Central Telangana Zone of Telangana state. Study Design: Ten (10) treatments in Randomized Block Design (RBD) with three (3) replications. Place and Duration of Study: Regional Agricultural Research Station (RARS), Professor Jayashankar Telangana State Agricultural University, Warangal, Central Telangana Zone of Telangana state. Experiment was conducted in the Kharif season in 2019-20 and 2020-21. Methodology: Ten chilli varieties namely Surajmukhi, Muchia, LCA-616, Bullet, Bor, Byadgi Kaddi, LCA-625, LCA-960, LCA-620, and Teja were sown in raised bed nursery and transplanted in the main field by following all the recommended package of practices. Observations were made on five randomly selected tagged plants from each variety and replicated for evaluation of characters like plant height, canopy spread, number of branches per plant, number of leaves per plant, chlorophyll content, fruit length, fruit width, ripe fruit colour, fruit shape at pedicel attachment, fruit position, fresh fruit weight, dry fruit weight, number of fruits per plant, dry fruit yield, mean thrips population and upward leaf curl index. Results: Out of ten chilli varieties, the dry chilli yield was found to be significantly higher in Teja (26.13 q/a) and on par results were noticed in Surajmukhi (25.12 q/a), whereas lowest dry chilli yield was observed in variety Bor (4.47 q/a). Thrips incidence and leaf curl index was found to be significantly lowest in Muchia. The trend indicates that the higher yield depends on the average fruit weight and number of fruits per plant. Conclusion: Among the ten chilli varieties under study, Teja and Surajmukhi yielded more dry chilli. Whereas the variety Muchia has shown lowest incidence of thrips and leaf curl index. Hence, these varieties have scope for future crop improvement programme.

Quality Traits and Nutritional Components of Cherry Tomato in Relation to the Harvesting Period, Storage Duration and Fruit Position in the Truss.

It is well known that the harvesting period and the storage duration have a significant effect on the quality characteristics of cherry tomato fruits. On the other hand, the effect of the fruit position in the truss has not been studied, as well as the relative contribution of each one of these factors on fruit quality. For this purpose, cherry tomato (Genio F1) whole trusses were harvested at the fruit red ripe stage during three periods. At each harvesting period, the first four (at the base of the truss) and the last four (at the top) fruits from each truss that was previously trimmed to 10 fruits, were stored at 12 °C for 0, 4 and 10 days. At the end of each storage duration, the external color, firmness, antioxidant capacity, pH and titratable acidity, as well as dry matter, soluble solid, total soluble phenol, lycopene, total carotenoid and β-carotene content, were determined. Analysis of variance (ANOVA) indicated that the harvesting period had the most significant effect on skin color parameters L * and C * and β-carotene, as well as on antioxidant capacity, total soluble phenols, dry matter and total soluble solids, while it also had an appreciable effect on titratable acidity. The storage duration had a dominant effect on firmness, total carotenoids and lycopene, while it had an appreciable effect on skin color parameter L * as well. On the other hand, the fruit position in the truss exerted an exclusive effect on ho and a */b * ratio skin color parameters and pH and an appreciable effect on titratable acidity.

Real-Time Tracking Based on Improved YOLOv5 Detection in Orchard Environment for Dragon Fruit

Highlights This method has achieved faster detection speed while maintaining accuracy. It is a real-time tracking method that can track dragon fruits in orchard environments in real-time. The introduction of an attention mechanism in the network provides good robustness to changes in lighting and target scale. Abstract. This article addresses the issue of dragon fruit real-time detection in orchard environments and proposes a real-time detection and tracking model for dragon fruit using an improved YOLOv5 object detection algorithm and Deep-sort object tracking algorithm. By applying real-time tracking to dragon fruit harvesting, the tracking algorithm provides timely feedback on the fruit's location, allowing for prompt correction of environmental issues that may affect the accuracy of the harvesting process. This approach enhances the robustness of the target positioning algorithm. First,based on the YOLOv5 object detection algorithm, the Convolutional Block Attention Module and Transformer self-attention mechanism are introduced to construct a YOLOv5s-DFT object detection model that is more suitable for dragon fruit detection. Next, Combining the Deep-sort multi-object tracking algorithm, this article proposes a real-time detection and tracking method for dragon fruit in the orchard environment. The YOLOv5s-DFT model was trained and experimented with using a self-built dataset. The trained model weight is only 19.26% of YOLOv7. The experimental result shows that, while ensuring detection accuracy, YOLOv5s-DFT has a faster detection speed in dragon fruit detection, with an average frame time of 0.01673 s, which is 0.00422 s faster than the original YOLOv5s. When tracking dragon fruit using the Deep-sort tracking algorithm, it can track dragon fruit at a speed of 47.08 frames per second. When utilizing the Deep-sort tracking algorithm to track dragon fruit, it achieves a tracking speed of 47.08 frames per second, enabling real-time acquisition of the fruit's position information. This technology provides technical assistance for the intelligent harvesting of dragon fruit and the intelligent management of dragon fruit orchards. Keywords: Dragon fruit, Improved YOLOv5, Orchard environment, Real-time tracking.

A Method of Grasping Detection for Kiwifruit Harvesting Robot Based on Deep Learning

Kiwifruit harvesting with robotics can be troublesome due to the clustering feature. The gripper of the end effector will easily cause unstable fruit grasping, or the bending and separation action will interfere with the neighboring fruit because of an inappropriate grasping angle, which will further affect the success rate. Therefore, predicting the correct grasping angle for each fruit can guide the gripper to safely approach, grasp, bend and separate the fruit. To improve the grasping rate and harvesting success rate, this study proposed a grasping detection method for a kiwifruit harvesting robot based on the GG-CNN2. Based on the vertical downward growth characteristics of kiwifruit, the grasping configuration of the manipulator was defined. The clustered kiwifruit was mainly divided into single fruit, linear cluster, and other cluster, and the grasping dataset included depth images, color images, and grasping labels. The GG-CNN2 was improved based on focal loss to prevent the algorithm from generating the optimal grasping configuration in the background or at the edge of the fruit. The performance test of the grasping detection network and the verification test of robotic picking were carried out in orchards. The results showed that the number of parameters of GG-CNN2 was 66.7 k, the average image calculation speed was 58 ms, and the average grasping detection accuracy was 76.0%, which ensures the grasping detection can run in real time. The verification test results indicated that the manipulator combined with the position information provided by the target detection network YOLO v4 and the grasping angle provided by the grasping detection network GG-CNN2 could achieve a harvesting success rate of 88.7% and a fruit drop rate of 4.8%; the average picking time was 6.5 s. Compared with the method in which the target detection network only provides fruit position information, this method presented the advantages of harvesting rate and fruit drop rate when harvesting linear clusters, especially other cluster, and the picking time was slightly increased. Therefore, the grasping detection method proposed in this study is suitable for near-neighbor multi-kiwifruit picking, and it can improve the success rate of robotic harvesting.

A Ground-based Platform for Reliable Estimates of Fruit Number, Size, and Color in Stone Fruit Orchards

Automatic in-field fruit recognition techniques can be used to estimate fruit number, fruit size, fruit skin color, and yield in fruit crops. Fruit color and size represent two of the most important fruit quality parameters in stone fruit (Prunus sp.). This study aimed to evaluate the reliability of a commercial mobile platform, sensors, and artificial intelligence software system for fast estimates of fruit number, fruit size, and fruit skin color in peach (Prunus persica), nectarine (P. persica var. nucipersica), plum (Prunus salicina), and apricot (Prunus armeniaca), and to assess their spatial and temporal variability. An initial calibration was needed to obtain estimates of absolute fruit number per tree and a forecasted yield. However, the technology can also be used to produce fast relative density maps in stone fruit orchards. Fruit number prediction accuracy was ≥90% in all the crops and training systems under study. Overall, predictions of fruit number in two-dimensional training systems were slightly more accurate. Estimates of fruit diameter (FD) and color did not need an initial calibration. The FD predictions had percent standard errors <10% and root mean square error <5 mm under different training systems, row spacing, crops, and fruit position within the canopy. Hue angle, a color attribute previously associated with fruit maturity in peach and nectarine, was the color attribute that was best predicted by the mobile platform. A new color parameter—color development index (CDI), ranging from 0 to 1—was derived from hue angle. The adoption of CDI, which represents the color progression or distance from green, improved the interpretation of color measurements by end-users as opposed to hue angle and generated more robust color estimations in fruit that turn purple when ripe, such as dark plum. Spatial maps of fruit number, FD, and CDI obtained with the mobile platform can be used to inform orchard decisions such as thinning, pruning, spraying, and harvest timing. The importance and application of crop yield and fruit quality real-time assessments and forecasts are discussed.

Changes in cytokinins and auxins levels in the rind of ‘Nules Clementine’ mandarin as related to the fruit position on the tree and the susceptibility to non-chilling rind breakdown disorder

• Phytohormonal changes in relation to citrus rind disorder was examined postharvest. • Fruit were harvested from inside and outside canopy position of the tree. • Endogenous levels of cytokinins (CK), auxins, isomers and conjugates were analysed. • Cis -zeatin-type CK concentrations were higher in outside canopy fruit postharvest. The role of phytohormones in mitigating or enhancing the incidence of non-chilling rind physiological disorder of citrus fruit is largely unknown. Therefore, this study attempted to elucidate cytokinins (CK) and auxin changes in ‘Nules Clementine’ mandarin fruit rind from different canopy positions during postharvest non-chilling cold storage regarding rind breakdown disorder (RBD). Fruit from the inside canopy (IC) and outside canopy (OC) positions of citrus trees were harvested at commercial maturity from Eastern Cape (EC) and Western Cape (WC) provinces of South Africa, respectively. Endogenous levels of CK, auxins, their isomers, and conjugates were analysed using ultra-performance liquid chromatography coupled with a triple quadrupole mass spectrometer (MS/MS) equipped with an electrospray interface. Cis -zeatin-type cytokinins were significantly higher in the OC fruit than in the IC fruit from both provinces during cold storage. Whereas dihydrozeatin concentration (the most abundant CK) was significantly higher in fruit from IC (326.10 and 29.38 nmol kg −1 ) than in OC (65.75 and 17.72 nmol kg −1 ) fruit before cold storage, respectively. Fruit without rind disorder had lower CK - conjugates than fruit with the disorder. The IAA concentration was higher in the IC fruit (344.15 nmol kg −1 ) than in the OC fruit (194.20 nmol kg −1 ) from EC at week 0 while the IAA concentration of OC fruit from WC was below the limit of detection and IC fruit had 53.20 nmol kg −1 at week 0. Fruit without the disorder had more IAA concentration than fruit with RBD while indole-3-acetylaspartic acid (IAAsp) was higher in fruit with RBD than fruit without the disorder, indicating the preventive role of IAA in the incidence of the disorder. This study is the first to record the negative effect of IAAsp in RBD and revealed the crucial role and synergistic role of CK, IAA and their isomers in RBD of ‘Nules Clementine’ fruit.

Improving bitter pit prediction by the use of X-ray fluorescence (XRF): A new approach by multivariate classification.

Bitter pit (BP) is one of the most relevant post-harvest disorders for apple industry worldwide, which is often related to calcium (Ca) deficiency at the calyx end of the fruit. Its occurrence takes place along with an imbalance with other minerals, such as potassium (K). Although the K/Ca ratio is considered a valuable indicator of BP, a high variability in the levels of these elements occurs within the fruit, between fruits of the same plant, and between plants and orchards. Prediction systems based on the content of elements in fruit have a high variability because they are determined in samples composed of various fruits. With X-ray fluorescence (XRF) spectrometry, it is possible to characterize non-destructively the signal intensity for several mineral elements at a given position in individual fruit and thus, the complete signal of the mineral composition can be used to perform a predictive model to determine the incidence of bitter pit. Therefore, it was hypothesized that using a multivariate modeling approach, other elements beyond the K and Ca could be found that could improve the current clutter prediction capability. Two studies were carried out: on the first one an experiment was conducted to determine the K/Ca and the whole spectrum using XRF of a balanced sample of affected and non-affected 'Granny Smith' apples. On the second study apples of three cultivars ('Granny Smith', 'Brookfield' and 'Fuji'), were harvested from two commercial orchards to evaluate the use of XRF to predict BP. With data from the first study a multivariate classification system was trained (balanced database of healthy and BP fruit, consisting in 176 from each group) and then the model was applied on the second study to fruit from two orchards with a history of BP. Results show that when dimensionality reduction was performed on the XRF spectra (1.5 - 8 KeV) of 'Granny Smith' apples, comparing fruit with and without BP, along with K and Ca, four other elements (i.e., Cl, Si, P, and S) were found to be deterministic. However, the PCA revealed that the classification between samples (BP vs. non-BP fruit) was not possible by univariate analysis (individual elements or the K/Ca ratio).Therefore, a multivariate classification approach was applied, and the classification measures (sensitivity, specificity, and balanced precision) of the PLS-DA models for all cultivars evaluated ('Granny Smith', 'Fuji' and 'Brookfield') on the full training samples and with both validation procedures (Venetian and Monte Carlo), ranged from 0.76 to 0.92. The results of this work indicate that using this technology at the individual fruit level is essential to understand the factors that determine this disorder and can improve BP prediction of intact fruit.

Impact of Cover Crops and Nitrogen on Cotton Fiber Cellulose and Quality Traits

ABSTRACT Fiber quality varies depending on the fruit position of the cotton plant. Nitrogen (N) availability can alter fruiting dynamics and, consequently, fiber quality. The current study evaluated the effect of cover crops and N availability on cellulose content and fiber quality in different plant positions. The experiment was carried out in Presidente Bernardes, São Paulo, Brazil. Experiment design was a split-plot in completely randomized block design with five replicates. Cover crops were allocated: Fallow (F), Velvet bean (VB), Ruzigrass (Urochloa ruziziensis) (R), Inoculated ruzigrass (IR) (Azospirillum), Ruzigrass + velvet bean (R+VB) and Inoculated ruzigrass + velvet bean (IR+VB); and N rates (80 and 120 kg ha−1) in the sub-plots. Cellulose content in the lower, middle, and upper third of the plants were evaluated at 15 and 30 days after anthesis (DAA) and at harvest. At 15 and 30 DAA, the cellulose content was higher in the lower third in most treatments, except in the treatment with ruzigrass (120 N), which was higher in the upper third. Fibers with higher quality and final cellulose content are in the lower parts of the plant, and IR+VB improved fiber quality by providing applications with lower rates of N.

Fruit pose recognition and directional orderly grasping strategies for tomato harvesting robots

The prospect of robotic harvesting in greenhouse tomato production has received much attention in the tomato industry in recent years, but research into the replacement of manual harvesting with this technology is still inadequate. There is still a large gap between robotic harvesting accuracy and harvesting efficiency compared to manual harvesting. The aim of this study is to improve the success rate and efficiency of robotic harvesting by accurately identifying the position of the tomato fruit and estimating the grasping pose. Based on a specially designed adsorption and clamping integrated manipulator, we develop the optimal sorting algorithm and fruit nearest neighbour positioning algorithm and design directional grasping and sequential picking control strategies that can reduce the impact of dense fruit on the accurate grasping of the manipulator. The results evaluated in the industrial greenhouse show that the YOLOv5m-based fruit bunch and fruit recognition accuracies are 90.2 % and 97.3 %, respectively. Based on the optimized picking strategy, the impact of collision on the manipulator's grasp was substantially reduced, and the fruit harvesting success rate increased to 72.1 %, while the average harvesting time reached 14.6 s for a single fruit. The research suggests that with moderate improvements in harvesting technology, the use of robotic systems in the tomato industry will gradually become a reality.

Identification and characterization of putative targets of VEGETATIVE1/FULc, a key regulator of development of the compound inflorescence in pea and related legumes.

Inflorescence architecture contributes to essential plant traits. It determines plant shape, contributing to morphological diversity, and also determines the position and number of flowers and fruits produced by the plant, thus influencing seed yield. Most legumes have compound inflorescences, where flowers are produced in secondary inflorescences (I2), formed at the flanks of the main primary inflorescence (I1), in contrast to simple inflorescences of plants like Arabidopsis, in which flowers are directly formed on the I1. The pea VEGETATIVE1/FULc (VEG1) gene, and its homologs in other legumes, specify the formation of the I2 meristem, a function apparently restricted to legumes. To understand the control of I2 development, it is important to identify the genes working downstream of VEG1. In this study, we adopted a novel strategy to identify genes expressed in the I2 meristem, as potential regulatory targets of VEG1. To identify pea I2-meristem genes, we compared the transcriptomes of inflorescence apices from wild-type and mutants affected in I2 development, such as proliferating inflorescence meristems (pim, with more I2 meristems), and veg1 and vegetative2 (both without I2 meristems). Analysis of the differentially expressed genes using Arabidopsis genome databases combined with RT-qPCR expression analysis in pea allowed the selection of genes expressed in the pea inflorescence apex. In situ hybridization of four of these genes showed that all four genes are expressed in the I2 meristem, proving our approach to identify I2-meristem genes was successful. Finally, analysis by VIGS (virus-induced gene silencing) in pea identified one gene, PsDAO1, whose silencing leads to small plants, and another gene, PsHUP54, whose silencing leads to plants with very large stubs, meaning that this gene controls the activity of the I2 meristem. PsHUP54-VIGS plants are also large and, more importantly, produce large pods with almost double the seeds as the control. Our study shows a new useful strategy to isolate I2-meristem genes and identifies a novel gene, PsHUP54, which seems to be a promising tool to improve yield in pea and in other legumes.

Rootstock vigor and fruit position in the canopy influence peach internal quality

Rootstock vigor and fruit position in the canopy influence peach internal quality

Programas comerciais de resfriamento rápido para manga ‘Rosa’: Impactos na qualidade da fruta

ABSTRACT This study aimed to verify the impact of commercial fast-cooling programs on physicochemical quality of ‘Rosa’ mangoes. The study was carried out in a mango packing house in the São Francisco Valley region, Petrolina, PE, Brazil, between August and October 2018. The experiment followed a randomized block design, with three replicates and one pallet per replicate. The treatments were organized in a factorial scheme 2 × 2 × 3 (two fast-cooling times [120 and 240 min] × two storage times x three positions in the pallet). Sixty mangoes were proportionally distributed in three layers at different heights on each of the six pallets. Three pallets were subjected to fast cooling for 120 min and the other three for 240 min. All fruits were stored for 7 and 14 days at 6 °C plus three days of shelf-life at 20 °C. Regardless of cooling program and fruit position in the pallet, the seven-eighths cooling time (SECT) was not attained. The titratable acidity of fruits under 240-min fast cooling decreased significantly after 14 days of cold storage. Fruit weight loss, pulp firmness, dry matter, and soluble solids content were not affected by the fast-cooling programs, both after storage and during shelf-life period. Commercial fast-cooling program can be performed for 120 min.

Real-time, highly accurate robotic grasp detection utilizing transfer learning for robots manipulating fragile fruits with widely variable sizes and shapes

The automatic picking, sorting, and packaging of fruits require robots to accurately detect the grasping position of fruits. However, accurate detection of grasping positions is challenging due to the diversity of fruit shapes and sizes. At present, research objects of grasping detection are mainly daily necessities and office items, and few studies on fruit-grasping detection are available. To solve these problems, four end-to-end detection models were designed based on three convolutional neural network architectures: Xception, MobileNetV3, and DenseNet. In addition, considering the large amount of data required for deep learning, data augmentation and transfer learning techniques were applied to improve model accuracy and generalization performance. The most widely applied evaluation criteria were used to evaluate the models, and the accuracy of the four models ranged within 83.86%–93.64%. All the models were capable of rapid real-time detection. To verify the robustness, the models were tested under different evaluation thresholds, and the results showed that the models performed well under higher evaluation criteria. Additionally, a dataset containing 4400 images of 11 common fruits was established due to the current lack of data for fruit grasp detection.

Marketing Channel Analysis of Oil Palm Fresh Fruit Bunches in Lubuk Barumun District, Padang Lawas Regency

The purpose of the research is to find out marketing channel analysis of oil palm fresh fruit bunches in Lubuk Barumun District, Padang Lawas Regency. The research was conducted in Lubuk Barumun District in Padang Lawas Regency, North Sumatra Province with the consideration that Padang Lawas Regency has potential in developing oil palm plantation areas. The research was carried out from January to February 2021. The sample collection of producer farmers was carried out by purposive sampling with the sample criteria being independent oil palm farmers. From the population of oil palm farmers in Padang Lawas Regency as many as 297 people, a sample of 10 percent was taken, namely 30 farmer respondents. Quantitative analysis is used to determine the Hirschman Herfindahl index on market structure, market behavior based on the price relationship between two markets at the same level and the magnitude of market performance based on marketing margin analysis, profit margin ratio, farmer's share, cost share, and profit share. The results showed that, the number of oil palm farmers is large while the number of sellers or intermediaries is only a few so that the marketing system for fresh fruit bunches of oil palm tends to be imperfect competition because there are many sellers but few buyers (oligopsony). Analysis of the Hirschman Herfindahl index, it is known that the market structure of oil palm fresh fruit bunches in Lubuk Barumun District, Padang Lawas Regency tends to lead to an imperfect competition market (oligopsony) which causes the bargaining position of oil palm fresh fruit bunches farmers to be in poor condition weak, namely as a price taker. The efficiency of an already efficient marketing channel is the one that shows the smallest value of marketing efficiency, namely in channel III (Ep < 5% efficient). Marketing channels I and II have not been efficient (Ep > 5% inefficient). Keywords: Marketing Channel, Oil Palm, Fresh Fruit Bunches .

A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard

Accurate count of fruits is important for producers to make adequate decisions in production management. Although some algorithms based on machine vision have been developed to count fruits which were all implemented by tracking fruits themselves, those algorithms often make mismatches or even lose targets during the tracking process due to the large number of highly similar fruits in appearance. This study aims to develop an automated video processing method for improving the counting accuracy of apple fruits in orchard environment with modern vertical fruiting-wall architecture. As the trunk is normally larger than fruits and appears clearly in the video, the trunk is thus selected as a single-object tracking target to reach a higher accuracy and higher speed tracking than the commonly used method of fruit-based multi-object tracking. This method was trained using a YOLOv4-tiny network integrated with a CSR-DCF (channel spatial reliability-discriminative correlation filter) algorithm. Reference displacement between consecutive frames was calculated according to the frame motion trajectory for predicting possible fruit locations in terms of previously detected positions. The minimum Euclidean distance of detected fruit position and the predicted fruit position was calculated to match the same fruits between consecutive video frames. Finally, a unique ID was assigned to each fruit for counting. Results showed that mean average precision of 99.35% for fruit and trunk detection was achieved in this study, which could provide a good basis for fruit accurate counting. A counting accuracy of 91.49% and a correlation coefficient R2 of 0.9875 with counting performed by manual counting were reached in orchard videos. Besides, proposed counting method can be implemented on CPU at 2 ∼ 5 frames per second (fps). These promising results demonstrate the potential of this method to provide yield data for apple fruits or even other types of fruits.

Fruit Quality Evaluation in The Maturation Process of Blueberries Using Image Processing

Blueberries' quality does not change uniformly during ripeness. Blueberries should be harvested fully ripened at the post-climacteric stage with an excellent indicator including consistent color, taste, and ease of removal from plant as excellent indicators. Therefore, the blueberries are not harvested until it has the desired blue color. The reliance on human perception on the fruit's taste and appearance might cause inconsistency and inaccurate judgment of the fruit maturation. This study aimed to develop an image processing algorithm capable of classifying blueberry maturity stages. The Bluecrop Northern highbush blueberry was harvested at five different stages of maturity based on visual grading of the fruit color (green, green-red, red, red-blue, and blue) from various fruit positions on the tree. Image processing with discriminant analysis accurately classified maturity stages at 98.3% accuracy. The image quality attributes of blueberries changed significantly at different maturity stages. Overall, most image quality attributes correlated strongly with well-performed blueberry physicochemical properties. This study showed that image processing during the blueberry maturation process could be a reliable and comprehensible method for estimating changes in color, shape, weight, and ultimately changes in specific physicochemical properties. This study also provided a practical evaluation of the maturity stages and physicochemical properties, which were predicted using image processing.

Increasing Fruit Weight by Editing a Cis-Regulatory Element in Tomato KLUH Promoter Using CRISPR/Cas9.

CRISPR/Cas-mediated genome editing is a powerful approach to accelerate yield enhancement to feed growing populations. Most applications focus on “negative regulators” by targeting coding regions and promoters to create nulls or weak loss-of-function alleles. However, many agriculturally important traits are conferred by gain-of-function alleles. Therefore, creating gain-of-function alleles for “positive regulators” by CRISPR will be of great value for crop improvement. CYP78A family members are the positive regulators of organ weight and size in crops. In this study, we engineered allelic variation by editing tomato KLUH promoter around a single-nucleotide polymorphism (SNP) that is highly associated with fruit weight. The SNP was located in a conserved putative cis-regulatory element (CRE) as detected by the homology-based prediction and the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). Twenty-one mutant alleles with various insertion and deletion sizes were generated in the LA1589 background. Five mutant alleles (m2+4bp, m3+1bp, m5–1bp, m13–8bp, and m14–9bp) showed a consistent increase in fruit weight and a significant decrease in the proportion of small fruits in all experimental evaluations. Notably, m2+4bp and m3+1bp homozygote significantly increase fruit weight by 10.7–15.7 and 8.7–16.3%, respectively. Further analysis of fruit weight based on fruit position on the inflorescence indicated that the five beneficial alleles increase the weight of all fruits along inflorescence. We also found that allele types and transcriptional changes of SlKLUH were poor predictors of the changes in fruit weight. This study not only provides a way of identifying conserved CRE but also highlights enormous potential for CRISPR/Cas-mediated cis-engineering of CYP78A members in yield improvement.