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Mulching with recycled paper has the potential to be used in agricultural production and can be adopted in strawberry cultivation. Therefore, the objective of this study was to evaluate the agronomic characteristics, water consumption and technical coefficients of strawberry cultivated with recycled paper as mulch. The treatments consisted of strawberry cultivation in lysimeters with recycled paper mulch and without soil cover. The recycled paper used was 187 μm-thick. The irrigation system was installed with drippers whose flow rate was 2 L h−1. Strawberry irrigation requirements were 317 and 394 mm, with and without mulch, respectively. Mulching with recycled paper did not have a significant effect on the average number of fruits, fresh fruit mass, fruit waste per plant, average fruit yield and water productivity. In relation to the technical coefficients, maximum values of the evaporation coefficient (Ke) of 0.40 (calculated with the evapotranspiration of the grass—L) and 0.28 (calculated with the Penman–Monteith ETo—PM) were obtained for OPM. The Kc values for the initial and full development stages were 0.31 and 0.84 (L) and 0.24 and 0.66 (PM), respectively. NDVI can be used to estimate strawberry Kc values. Recycled paper as mulch reduces the water consumption of strawberry crops and does not alter the agronomic characteristics. [ABSTRACT FROM AUTHOR]

Black plastic film mulching is widely practiced in crop production, especially in horticultural systems, because of its multiple benefits. However, its post-usage polluting residues and limited availability to farmers at all places cause concern. The objective of this study was to evaluate in a field experiment the potential of the commonly available stationery-purpose poly-coated brown wrapping sheet mulching in bhendi [Abelmoschus esculentus (L.) Moench] in comparison with black plastic film mulching. Observations made on plant height and fruit yield, soil temperature and soil microbial population revealed that the less-polluting poly-coated brown wrapping sheet mulching was as good as the black plastic film mulching in performance which is discussed in the light of increase in plant growth and yield, moisture conservation and weed suppression, root-zone temperature moderation and termite activity. [ABSTRACT FROM AUTHOR]

We looked into bagging and spraying as potential methods for increasing the yield and quality of Manfaloty pomegranate trees. During the 2020 and 2021 seasons, 7-year-old trees were used in the investigation. They were grown in sandy, calcareous soil with high temperatures. Six treatments were used to examine the effects on fruit yield and quality parameters at harvest time in the first week of September. These treatments included foliar spraying with kaolin and potassium silicate treatments, polypropylene bags and bagging with white and yellow paper. Results indicated that white paper bagging resulted in the lowest percentage of fruit cracking and sunburn compared to the control and other treatments. White paper bagging increased fruit yield, weight, length, and diameter. Additionally, compared to the other treatments, the application of kaolin spray increased the biochemical contents of the fruit (TSS, total sugars, vitamin C, anthocyanin, and juice) while lowering acidity and tannins. We concluded that using kaolin spray and white paper bags could enhance the yield and quality of Manfaloty pomegranate grown under challenging conditions. [ABSTRACT FROM AUTHOR]

Despite being historically renowned as a significant date producer, both the Arab world and Iraq have experienced a noticeable decline in date palm production in recent years. This decrease in output can be attributed to the sluggish integration of advanced techniques that could enhance and maintain high-quality yields. In this regard, an experiment was conducted at the Al-Rabee station on date palm cultivar Khastawi under the randomized complete blocks design with three replications, to study the effect of four treatments of bagging: B0 - Without bagging, B1 - Rocklin, B2- Agryl and B3 - Paper bags and four bagging periods: T0 - 0, T1 - 40, T2 - 50 and T3 - 60 days, and their interactions on the fruits characteristics of date palm cv. Khastawi. The results showed that T1B3 treatment gave the highest values in fruit set (86.55%), while T3B2 treatment was superior in respect to fruit weight (7.260g), seed weight (0.6300g), fresh weight (6.630g) and fruit diameter (1.8533cm). The lowest values of fruit characteristics were recorded in the control treatment. Therefore, we recommend using bagging fruits to improve fruit set, bunch yield and fruit quality. [ABSTRACT FROM AUTHOR]

Fruit bagging protects fruits from pest infestation and multiple biotic and abiotic stresses during development without reducing yield. It also can potentially improve the quality and market value of fruits. Hence, the present study aimed to determine the effect of pre-harvest fruit bagging on the yield and quality of tomato (Solanum lycopersicum) cv. BARI Tomato-2. The experiment was laid out in a Randomized Complete Block Design (RCBD) with three replications that constituted the various treatments-T0: Control (no bagging), T1: White paper bag (single layer), T2: Brown paper bag (double layer), T3: Non-woven fabric bag, and T4: Jute bag from October 2021 to March 2022. The results showed that the non-woven fabric bag had the highest fruit length (57.79 mm), diameter (57.81 mm), number of fruits plant-1 (55.9), yield (4.06 kg plant-1), vitamin C (24.63mg/ 100g-1), and lycopene content (0.12 mg g-1). The maximum insect incidence (18.41%) was recorded in control and the minimum was counted in T3 (2.17%). The highest total soluble solid (6.73%0Brix) and dry matter (8.63%) were observed in a white paper bag. The highest mean color score (6.08), firmness (26.81%), and shelf life (13 days) found in fabric bags outperformed non-bagged fruits. The fabric bag showed maximum net income (21.12 TK) and the highest benefit: cost ratio (2.36). Thus, the fabric bag was the best for improving the physical and chemical qualities of tomatoes (Solanum lycopersicum) among the various fruitcovering materials. [ABSTRACT FROM AUTHOR]

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This study was carried out in two different locations (L1: Yüzbaşılar village and L2: TUAM (Iğdır University Agricultural Application and Research Center) in 2020 to determine the effect of different waste mulch materials on weed control and tomato yield. In the study, five different mulch materials (tree of heaven leaves, fine sawdust, wood chips, shredded paper and sheep wool) were used. Each plot has weedy and weed-free control plots. The effects of mulching on weeding suppression, weed dry weight, yield (kg/da), fruit weight (gr) and number of fruit per plant were investigated. Three counts were made to determine the weed density in the plots. As a result of the study, a total of 17 weed species belonging to 9 families were determined in the experimental areas. As a result of the counts, it was observed that Sorghum halepense L., Convolvulus arvensis L. and Xanthium strumarium L. weeds emerged the most in the plots where the mulch materials were applied. Depending on the location, the lowest density among the weed density counts was obtained from the paper plots. In the study, the lowest average weed dry weight among the mulch materials was shredded paper (L1: 97.63; L2: 73.12 gr/m²), the highest average was tree of heaven (L1: 191.87; L2: 165.27 gr/m²) plots. As a result of the study, the best results in tomato yield (L1: 6.078.50; L2: 6.807.87 kg/da) and fruit weight (L1: 142.67; L2: 147.35 gr)) were obtained in the shredded paper mulch applied plots. The highest number of fruits per plant was obtained in the use of wood chips (L1: 45.774.83 pieces) and tree of heaven leaves (L2: 46.627.41 pieces). The fact that mulch applications give better results than control groups suggests that mulches will be much more beneficial for controlling weeds and improving fruit yield and quality. [ABSTRACT FROM AUTHOR]

Accurately obtaining the length, quantity and distribution of fruit branches plays an important role in orchard irrigation management, disease control and improving fruits' yield and quality. Recently, edge computing has been proposed for digital orchard management as it can increase computing power for computationally intensive applications. However, due to the diversity of fruit tree morphological structures and the complexity of the planting environment, the traditional method of obtaining fruit tree phenotypes with centralized computing on cloud servers faces many challenges in terms of efficiency and accuracy. In this paper, we propose a hierarchical growing method (HG) suitable for deployment at the edge to achieve semantic segmentation and instance segmentation of fruit tree point clouds at the organ scale. Furthermore, extract fruit trees phenotypic trait at the organ scale based on the result of point clouds segmentation. Numerous experiment show that the proposed HG method can efficiently carry on instance segmentation of branches and phenotypic trait extraction by the joint analysis and processing of point cloud data. The MAE, RMSE and IOU of the primary branches reach 0.025 m, 0.113 and 0.720, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Temas Agrarios is the property of Universidad de Cordoba and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dragon fruit stem disease significantly affects both the quality and yield of dragon fruit. Therefore, there is an urgent need for an efficient, high-precision intelligent detection method to address the challenge of disease detection. To address the limitations of traditional methods, including slow detection and weak micro-integration capability, this paper proposes an improved YOLOv8-G algorithm. The algorithm reduces computational redundancy by introducing the C2f-Faster module. The loss function was modified to the structured intersection over union (SIoU), and the coordinate attention (CA) and content-aware reorganization feature extraction (CARAFE) modules were incorporated. These enhancements increased the model's stability and improved its accuracy in recognizing small targets. Experimental results showed that the YOLOv8-G algorithm achieved a mean average precision (mAP) of 83.1% and mAP50:95 of 48.3%, representing improvements of 3.3% and 2.3%, respectively, compared to the original model. The model size and floating point operations per second (FLOPS) were reduced to 4.9 MB and 6.9 G, respectively, indicating reductions of 20% and 14.8%. The improved model achieves higher accuracy in disease detection while maintaining a lighter weight, serving as a valuable reference for researchers in the field of dragon fruit stem disease detection. [ABSTRACT FROM AUTHOR]

The management of global food security is one of the major issues of concern to the international community today. Ensuring the stability of food sources and preventing crop pests and diseases are crucial in maintaining social stability and promoting economic development. In modern agriculture, computer vision has emerged as a tool to aid in pest and disease prevention. For instance, when calculating the overall fruit yield of fruit trees and identifying and categorising pests and diseases, traditional neural networks tend to preserve duplicate data during image prediction. Traditional neural networks store unnecessary information when predicting images, leading to more classification calculations and thus higher computing costs. By utilising the concept of deep compressed perception, classification, and other operations can be carried out on compressed data. In this paper, combining compressed sensing theory and traditional neural network techniques, a novel deep compressed sensing network model called CSLSNet is proposed. The model utilizes a parallel convolution and residual structure comprising of convolution, the LR module, and the LSR module. The model directly categorizes images in the compressed domain, leading to decreased computation and a reduction in the number of model parameters. By comparing experiments using different SR (sampling rates) and traditional image compression methods alongside existing network models, this paper demonstrates that our model achieves higher classification accuracy under the same experimental conditions. Also, in fewer training cycles, the loss trend of the proposed model rapidly converges, and the loss curve becomes smoother. The results show that, at a sampling rate of 0.5, our model, CSLSNet, achieves an accuracy of 90.08%. In contrast, other networks involved in the comparison, such as CSBNet and AlexNet, achieve only 84.81% and 86.5%, respectively. [ABSTRACT FROM AUTHOR]

Apple leaf diseases without timely control will affect fruit quality and yield, intelligent detection of apple leaf diseases was especially important. So this paper mainly focuses on apple leaf disease detection problem, proposes a machine vision algorithm model for fast apple leaf disease detection called LALNet (High-speed apple leaf network). First, an efficient sacked module for apple leaf detection, known as EALD (efficient apple leaf detection stacking module), was designed by utilizing the multi-branch structure and depthseparable modules. In the backbone network of LALNet, (High-speed apple leaf network) four layers of EALD modules were superimposed and an SE (Squeeze-and-Excitation) module was added in the last layer of the model to improve the attention of the model to important features. A structural reparameterization technique was used to combine the outputs of two layers of deeply separable convolutions in branch during the inference phase to improve the model’s operational speed. The results show that in the test set, the detection accuracy of the model was 96.07%. The total precision was 95.79%, the total recall was 96.05%, the total F1 was 96.06%, the model size was 6.61 MB, and the detection speed of a single image was 6.68 ms. Therefore, the model ensures both high detection accuracy and fast execution speed, making it suitable for deployment on embedded devices. It supports precision spraying for the prevention and control of apple leaf disease. [ABSTRACT FROM AUTHOR]

Fruit tree improvement has taken great strides by roping in improved and efficient biotechnological tools to increase fruit yield and quality to meet local and export demands. For the past decade, the RNA-Seq tool has successfully been used in fruit tree improvement programs to identify genes, dissect complex traits, and understand different molecular pathways and differential expression of genes. However, despite their growing importance in food and nutrition security, medicinal uses, and climate change mitigation strategies, very little has been done to improve the pomological traits of African indigenous fruits, especially at the molecular level. African indigenous fruit trees exhibit unexplained variation in flowering, fruit load, fruit size, fruit ripening, fruit taste, fruit nutritional composition and shelf-life. The booming local commercial companies and export markets are demanding consistent quality indigenous fruits. This has necessitated the need for fast and effective tools that will hasten the understanding and improvement of fruiting qualities. The improvement of fruiting and fruit qualities will go a long way in accelerating the domestication and commercialization of African indigenous fruit trees. This review paper gives molecular biology insights on how RNA-Seq has been successfully used in fruit improvement of exotic fruits through gene identification, comparative transcriptome analysis under different conditions, and understanding molecular pathways that influence important pomological traits. The review article also unearths opportunities where RNA-Seq can improve our knowledge and improvement of undesirable traits common in African indigenous fruits. [ABSTRACT FROM AUTHOR]

Field experiments were carried out to evaluate the effects of different soil mulch materials and herbicides on growth and yield of tomato and weed control at two locations in Jordan during the 2016 and 2017 growing seasons. Treatments included different new nonwoven biodegradable UV-treated polypropylene fabrics, paper, black plastic, animal manure, straw mulches, five soilapplied herbicides, and weed-free and weed-infested controls. On average, weed competition with tomato for the entire growing season reduced marketable fruit yield by 58.6% of the weed-free control. Animal manure, black/white mulch/double face 45 g/m2 (GSM) density (B/WMD45) and paper mulches resulted highest tomato growth and fruit yield at the University of Jordan Campus. Other polypropylene treatments gave similar yield to that of plastic mulch while all mulch treatments gave higher yield than herbicides. Of the herbicides, metribuzin and trifluralin treated plots produced the lowest fruit yield. Similar trends for tomato yield and weed growth were also obtained at the Jordan Valley Research Station except for black plastic that gave the highest fruit yield but not significantly different from that of B/WMD45, paper and weed-free control. Among herbicides, oxyfluorfen resulted highest yield but differences between herbicide treatments were not significant. The highest tomato growth and fruit yield were from paper, black plastic, B/WMD45, and animal manure treatments and the lowest for herbicides and weed-infested treatments. B/WMD45 may be considered as a suitable substitute for black plastic in tomato fields. [ABSTRACT FROM AUTHOR]

In Romania, kiwi is a new fruit specie and the creation, testing and introduction of winter hardy genotypes, adapted to the local harsh climate conditions represent a priority. The first kiwifruit orchards with A. deliciosa and A. arguta were planted in Romania in 1993, at Ostrov (Constanța County), on the border of the Danube River. In the same year, a common Italian-Romanian kiwifruit breeding program was initiated at the Faculty of Horticulture within the University of Agronomic Sciences and Veterinary Medicine of Bucharest. Since 1993, research has been carried out to determine the best methods of propagation, growing and kiwifruit orchard management. Genotypes phenology was studied every year in comparison with the climatic data. In parallel, physical and biochemical fruit characteristics were evaluated after ripening and during the storage. After some years of observations and tests, three kiwiberry elites were selected: ‘Vip Green’ (R8P23), ‘Vip Red’ (R8P20) and ‘Green Delight’ (R8P1). Other intra and interspecific crossings using A. deliciosa and A. chinensis were made and from the initial hybrids, some selected genotypes as R0P13, R1P9, R1P8 and R1P12, have good fruit characteristics and yield. The new selections have to be registered as cultivars and can be successfully cultivated on commercial orchards and in private gardens. This paper presents few results of the Actinidia spp. Romanian breeding program. Some fruit quality characteristics of the new kiwi selections and hybrids as average weight, fruit shape index, soluble solids content, dry matter, acidity and ascorbic acid are detailed. After more than two decades of research, it was demonstrated that A. deliciosa and A. chinensis can be grown in Romania in peach favorable areas, while A. arguta (kiwiberry or baby kiwi) can cover larger areas, suitable for plum cultivation. [ABSTRACT FROM AUTHOR]

Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature maps. Additionally, the SPPCSPC module is optimized to preserve target area features as much as possible under different receptive fields, and the Soft-NMS algorithm is employed to reduce the likelihood of missing detections in overlapping regions. The model is trained on a diverse dataset collected from real-world field settings. Upon validation, the improved YOLOv7-E object detection algorithm achieves an average precision and recall of 91.4% and 89.8%, respectively, in inflorescence detection under various time periods, distances, and weather conditions. The detection time for a single image is 80.9 ms, and the model size is 37.6 Mb. In comparison to the original YOLOv7 algorithm, it boasts a 4.9% increase in detection accuracy and a 5.3% improvement in recall rate, with a mere 1.8% increase in model parameters. The YOLOv7-E object detection algorithm presented in this study enables precise inflorescence detection and localization across an entire tree at varying distances, offering robust technical support for differentiated and precise blossom thinning operations by thinning machinery in the future. [ABSTRACT FROM AUTHOR]

The accurate identification of citrus fruits is important for fruit yield estimation in complex citrus orchards. In this study, the YOLOv7-tiny-BVP network is constructed based on the YOLOv7-tiny network, with citrus fruits as the research object. This network introduces a BiFormer bilevel routing attention mechanism, which replaces regular convolution with GSConv, adds the VoVGSCSP module to the neck network, and replaces the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv) in the backbone network. The improved model significantly reduces the number of model parameters and the model inference time, while maintaining the network's high recognition rate for citrus fruits. The results showed that the fruit recognition accuracy of the modified model was 97.9% on the test dataset. Compared with the YOLOv7-tiny, the number of parameters and the size of the improved network were reduced by 38.47% and 4.6 MB, respectively. Moreover, the recognition accuracy, frames per second (FPS), and F1 score improved by 0.9, 2.02, and 1%, respectively. The network model proposed in this paper has an accuracy of 97.9% even after the parameters are reduced by 38.47%, and the model size is only 7.7 MB, which provides a new idea for the development of a lightweight target detection model. [ABSTRACT FROM AUTHOR]

Floral morphology and fruit diversity are two essential attributes of a plant to establish mutualistic association with pollinators. Pollinators will have a direct influence on fruit setting and yield. The present paper represents the floral morphology and fruit diversity of six wild melon genotypes (Cucumis melo ssp. agrestis). The staminate and pistillate flowers were analysed for their size, sepal and petal colour and pedicel length. It was observed that female flowers of wild genotypes were longer than male flowers, however male flowers possessed larger diameter, longer pedicel length and corolla length over female flowers. Among the six genotypes studied, HUB-13 produced maximum male flowers per vine (156.75), longest female flower (5.33 cm), longest pedicel and corolla and largest ovary (6.11 mm diameter). Genotype HUB-4 produced maximum female flowers per vine (39.50) with lower male to female sex ratio (2.65:1) and recorded longest male flower length (4.43 cm). Genotype, HUB-2 recorded lowest flower length (2.40 cm, 2.85 cm) and diameter, smallest corolla length (1.25 cm, 1.13 cm) and pedicel length (1.05 cm and 0.50 cm) for male and female flowers respectively. The sepals and petals were green and yellow in colour respectively with varied intensity. The genotype, HUB-9 recorded maximum ovary length (1.83 cm), fruit weight (86.03 g), fruit yield per vine (1.98 kg) and fruit yield per hectare (9.48 t/ha). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification. [ABSTRACT FROM AUTHOR]

Grapevine (Vitis vinifera L.) is one of the most widespread and economically important fruit crops in the world based on its capacity to produce high yields of quality fruit, hectares cultivated and its ability to grow in a wide range of climates and soils. However, it is greatly exposed to a wide variety of pathogens, affecting production and fruit quality. Among the potential threats, Allorhizobium vitis, the causal agent of grapevine crown gall represents a limiting factor in grape production worldwide. It causes vine decline and mortality especially in young vineyards and orchards with important economic losses. Owing of the systemic survival of Allorhizobium vitis in grapevine, copper bactericides and antibiotics are ineffective, and they are able to kill only the bacterium on contact. Therefore, the knowledge of pathogen, effective control and prevention strategies, and sensitive detection methods of pathogen are needed to improve the management of the disease. This review highlights the current state of research and the major acquisitions in this field and provides efficient procedures for isolating from tumors and soil. In addition, this paper discusses the different strategies used for the management of grapevine crown gall along with their drawbacks. Moreover, detection methods for rapid and proper identification of the disease bacteria were provided to enhance the efficiency of control measures and prevent the spread of the pathogen. [ABSTRACT FROM AUTHOR]

Lychee is an economically important crop with widespread popularity. However, lychee diseases significantly impact both the yield and fruit quality of lychee. Existing lychee disease detection models face challenges such as large parameter sizes, slow processing speeds, and deployment complexities. To address these challenges, this paper proposes an improved lightweight network, named YOLOv7-MGPC (YOLOv7-Mosaic-GhostNet-Pruning-CBAM), that enables real-time lychee disease detection. In this study, we collected datasets of lychee diseases, covering four types of leaf diseases, and employed Mosaic data augmentation for data preprocessing. Building upon the YOLOv7 framework, we replaced the original backbone network with the lightweight GhostNetV1 and applied channel pruning to effectively reduce the parameter overhead. Subsequently, an attention mechanism called CBAM was incorporated to enhance the detection accuracy. The resultant model was then deployed to edge devices (Nvidia Jetson Nano) for real-world applications. Our experiments showed that our enhanced YOLOv7 variant outperforms the original model by a large margin, achieving a speed increase from 120 frames/s to 217 frames/s while maintaining an accuracy of 88.6%. Furthermore, the parameter size was substantially reduced from 36.5 M to 7.8 M, which firmly demonstrates the effectiveness of our methods in enabling model deployment on edge devices for lychee disease detection. [ABSTRACT FROM AUTHOR]

Orchard thinning can avoid biennial bearing and improve fruit quality, which is a necessary agronomic section in orchard management. The existing methods of artificial fruit thinning and chemical spraying are no longer suitable for the development of modern agriculture. With the continuous acceleration of the construction process of modern orchards, blossom thinning mechanization has become an inevitable trend in the development of the orchard flower and fruit management. Based on relevant reports in the past 20 years, the paper discusses the current level of development of mechanized blossom thinning technologies and equipment in orchards from three aspects: mechanism research, machine development, and intelligent upgrading. Firstly, for thinning mechanism research, three directions were investigated: the rope flexible hitting force, thinning agronomic requirements, and the fruit tree growth model between thinning and fruit yields. Secondly, for marketable machine developments, two types of machines were investigated: the hand-held thinner and tractor-mounted thinner. The hand-held thinner is mainly suitable for traditional old orchards with a messy canopy structure, especially in the interior and top of the canopy. The tractor-mounted thinner is mainly suitable for orchards with the same crown structure, such as the hedge type, trunk type, and V-type. Thirdly, for equipment intelligent upgrading, the research of the intelligent detection algorithm for inflorescence on the fruit tree was investigated, for species including the apple, pear, citrus, grape, litchi, mango, and apricot. Finally, combining the advantages and disadvantages of the research, the authors propose thoughts and prospects, which can provide a reference for the design and applications of orchard mechanized blossom thinning. [ABSTRACT FROM AUTHOR]

Objective Pre-treating water prior to irrigation is an innovative technology increasingly used in modern irrigation. This paper compares the effect of irrigation using aerated (A) and magnetized (M) water on growth, yield and fruit quality of Gala apple in Xinjiang province. Method The experiment was conducted in an apple orchard, with freshwater irrigation treated as the control (CK). In each treatment, we measured the growth of the apple in different growth stages, as well as its ultimate fruit yield and quality. Result All irrigations significantly improved growth, fruit yield and quality of the apple. Compared with CK, aeration combined with magnetization increased the final shoot length and fruit size by 24.6% and 61.4%, respectively, while aerating the water alone increased leaf area index by 24.7%. Compared to CK, Magnetization and aeration working separately or in combination increased fruit yield by 19.2%-35.5%; their combination increased the fruit yield up to 37 239.93 kg/hm2. Compared with CK, magnetization and aeration working alone or in combination increased fruit hardness, soluble solid content, soluble sugar and Vc contents by 4.5%-7.9%, 13.9%-15.98%, 6.6%-28.2% and 15%-19.7%, respectively, and reduced detectable acid by 7.08%-18.35%. On average, magnetization- aeration combination was most effective in improving fruit yield and quality. Conclusion Irrigating the apple using the combination of magnetized and oxygenated water in Xinjiang can promote apple growth and improve its fruit yield and quality. [ABSTRACT FROM AUTHOR]

Copyright of Temas Agrarios is the property of Universidad de Cordoba and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops' yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions. [ABSTRACT FROM AUTHOR]

Fire blight (FB) caused by Erwinia amylovora, is a bacterial plant disease that poses a significant threat to pome fruit production worldwide. It can cause extensive losses in fruit yields, damage orchards and lead to trade restrictions on fruit exports. Fire Blight (FB) can also affect the longevity and productivity of fruit trees, further exacerbating the economic burden on growers and increasing management expenses. Because Erwinia amylovora is a regulated pest in most countries of the EPPO region its phytosanitary monitoring is mandatory in order to prevent the disease extension and new outbreaks. The inspections were required during the growing season (from after flowering until late summer), when the signs were obvious to find the disease. Thus, during 2022 a phytosanitary monitoring plan of Fire Blight was organized by Phytosanitary Office from Dolj County, Romania and the results emphasized that the apple area infected with Erwinia amylovora represents 3.85% from the total apple trees area (1,558 ha), 27% from the total surface of pear trees (36 ha) and 100 % for quince trees (2 ha). Comparatively with the results of Fire Blight monitoring realized previous year (2021), in 2022 was identified one new outbreak, but only for apple trees. Also, among all assessed trees (348) 260 trees include Malus domestica (apple). Among the measures applied for Fire Blight control the most effective were reducing the bacterial inoculum by manual removal of infected shoots and even uprooting of trees. [ABSTRACT FROM AUTHOR]

The aim of this study is to present the behaviour of new apple scab resistant varieties cultivated in Bucharest area. Apple scab (Venturia inaequalis) is an important and dangerous pathogen, specific treatment being required and, in many cases, the products exceed the new pesticides limits. Every year, new resistant varieties are released by breeders and before their spread into commercial orchards, they need to be tested in specific growing conditions. In the Didactic Experimental Orchard of the Faculty of Horticulture within USAMV of Bucharest more than 25 apple cultivars are planted and monitored. Most of the trees are grafted of M9 rootstock, planted at 3.5 x 1.0 m and led as Vertical Axe. The soil is maintained covered with a mixture of perennial grasses on the interrow and clean with herbicide on the row. Drip irrigation is applied. The paper presents the comparative results focused on the biometrical data like: average tree height, type, number and average length of fruiting shoots, trunk cross sectional area. For each variety fruit yield was calculated. Basic fruits analyses at the harvest moment are presented. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to provide a case study of a capacity building project and critical reflection in relation to transitioning to a sustainable food city. Design/methodology/approach: A case study research approach was adopted involving two research initiatives: first, a survey to elicit stakeholders' understanding of sustainable local food, with a view to creating a shared agenda and informing future strategic direction and second a combination of research approaches, including paired discussions, generation of pictorial outputs and a workshop, aimed to inform the future vision and mission of the Partnership. Findings: Collaboration with stakeholders through a variety of research initiatives has facilitated the development of a sustainable food city partnership, with the overarching aim of achieving a transition towards a more sustainable food system. Moreover, collaboration has contributed to the transition of the Partnership to ensure sustainability and continuity after the initial funding stage. Research limitations/implications: While universities have an important role to play in guiding direction and shaping new community initiatives for sustainability in their regions, the challenges, resources and time involved may be under-estimated; these projects take considerable time to yield fruit. Practical implications: The findings of the study will be of interest to those working in the community to promote education for sustainable development and better food systems. Originality/value: This paper addresses a gap in the literature in relation to universities and their collaboration with key stakeholders in building capacity and contributing to local sustainability transitions. [ABSTRACT FROM AUTHOR]

The quince is a fruit tree species that occupies 2% of the total orchard area in Romania. It can be utilized considering that quince is a fruit species used for ornamental purposes, for protective hedges, as a pollen-rich plant, for obtaining rootstock seedlings, in medicine, and the food industry. Following the identification of eco-pedological factors influencing the growth and development of quinces and the implementation of the stages underlying quince cultivation technology, it has been demonstrated that, in the pedoclimatic conditions of South Western Romania, establishing and exploiting a quince orchard is a highly profitable and interesting business with low investment and high returns. This fruit species yields abundantly, produces a large quantity of fruits, the production costs are low and can be recovered in a short time, the inter-row spaces can be used for intermediate crops, and the attractive selling price and the scarcity of quinces on the market provide the entrepreneur with an attractive income, making the business economically efficient. [ABSTRACT FROM AUTHOR]

Selenium (Se) is a beneficial element for plant growth and development. In this study, three-year-old potted 'Qingcui' plums were treated with Na2 SeO4 (Se6+) or selenomethionine (SeMet Se2-) to explore the effect of Se on the plant growth, fruit quality, and 5-hydroxytryptophan (5-HTP) metabolism. Flower and fruit numbers, fruit quality and yield, Se content, and 5-HTP metabolites and enzymes were detected. The results showed that the flower and fruit numbers, and yield were significantly increased by the Se application. There were no significant differences in the fruit diameters, fruit mass, edible part ratio, titratable acids, water content, and solid acid ratio among the treatments. The total soluble solids, soluble protein, and malondialdehyde contents under the Se6+ treatment showed no significant difference compared to the Se2- treatment, but they were significantly higher than these under control by 16.71%, 39.13%, and 36.27%, respectively. The Se application markedly increased plant the Se content, and Se contents in the roots and leaves, or the fruits were significantly larger by the Se6+ treatments than the Se2- treatment. The leaves' pigment contents under the Se2- treatments were significantly larger than those under the control or Se6+ treatment. Tryptophan was not significantly influenced, the 5-HTP and 5-methoxytryptophan contents were reduced in the roots, and increased in the leaves, and the serotonin content was only significantly increased in the roots by the Se treatments. The tryptophan hydroxylase and hydroxyindole-O-methyltransferase levels were slightly influenced, and the tryptophan decarboxylase level in the roots or fruits was significantly increased by the Se treatments. The Se application had beneficial effects on the plant growth, fruit quality, and Se content, especially in the Se6+ treatment, and influenced the 5-HTP metabolism. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] Water and nutrients combine to mediate crop acquisition of resources from soil. This paper studies the integrated effect of irrigation and fertilization on yield and fruit quality, as well as water use efficiency of substrate-cultured tomato. [Method] The experiment was conducted in a solar greenhouse at Hexi in northwestern China. The substrates used in the experiment comprised 50% of corn straw, 30% of slag, and 20% of cow dung and biological microbes. There were four irrigation-fertigation treatments: N 615 kg/hm², P2O5 315 kg/hm², K2O 750 kg/hm², and irrigation 6 000 m³/hm² (T0, used by local farmers); N 435 kg/hm², P2O5 150 kg/hm², K2O 600 kg/hm², and irrigation 4 500 m³/hm² (T1); reducing the irrigation and fertilization in T0 by 10% (T2), reducing the irrigation and fertilization in T1 by 10% (T3). For each treatment, we measured fruit yield and quality, and its water use efficiency. [Result] Irrigation and fertilization affected fruit yield and quality, and the water-fertilizer utilization efficiency significantly. The biomass, oxidase activity, proline, quality indexes, yield, and water -fertilizer utilization efficiency of the crop were the highest in T3, least in T0, with T1 and T2 between. T3 gave the highest commercial-standard tomato fruits (87%) and best fruit flavor, with the ratio of soluble sugar to organic acids being 5.13. Compared to T0, other treatments increased the fruit yield by 3.6% to 27.8%. T3 gave the highest yield (152 489.25 kg/hm²), the highest irrigation water use efficiency and partial fertilizer productivity, which were 30.8 kg/m³ and 116.9 kg/kg, respectively. [Conclusion] Irrigating 4 950 m³/hm² of water coupled with fertilizing 480 kg/hm² N, 165 kg/hm² P2O5 and 660 kg/hm² K2O was optimal to improve growth, fruit yield and quality, and water-fertilizer use efficiency of the substrate-cultured tomato grown in solar greenhouses in the arid areas in Hexi, northwestern China. [ABSTRACT FROM AUTHOR]

Rising tropospheric ozone (O3) and temperature, the two most important stresses caused due to climate change. Both stresses have now become foremost concerns for production of tomato worldwide due to their role in immensely afflicting productivity. These stresses have been found to occur concomitantly, and tomato is very sensitive to both heat stress and elevated O3. This paper reviews the individual effects of high temperature and elevated O3 on the overall performance of tomato and the defense strategies responsible for varying sensitivities among the genotypes. High temperature influences photosynthesis directly, and Fv/Fm ratio was widely used in identifying the sensitivity of tomato cultivars. It also affects pollen viability and germination, which ultimately results in the decline of fruit set and yield. Phytohormones are crucial for reducing the negative impacts of high temperature on tomato. Besides, several heat-shock proteins and factors and stress-regulated genes were upregulated in tomato under heat stress. An increase in surface O3 level causes reductions in photosynthesis, biomass accumulation, pollen viability, fruit quality, and yield. Various molecular and physiological processes deployed during elevated O3 conditions are believed to be intervened by signaling molecules like ethylene and jasmonic acid. The review concludes that responses of tomato to both the stresses are somewhat similar, but their nature and effects may vary with intensity and duration of stresses, genotypes, and other environmental factors. Since these stresses occur simultaneously, their combined effects need to be understood to avoid the yield losses under future scenarios of climate change. [ABSTRACT FROM AUTHOR]

For yield estimation, it is crucial to achieve quick and precise identification of mango fruits in the natural situations and surroundings. Using imaging with computer vision to accurately detect and count fruits during plant growth is important. It is not just because it is a vital step toward automating procedures like harvesting but also for minimizing labour-intensive human assessments of phenotypic information which can be useful for the farmer. Fruit farmers or cultivators in agriculture would benefit greatly from being able to track and predict production prior to fruit harvest. In order to make the best use of the resources needed for each individual site, such as water use, fertiliser use, and other agricultural chemical compounds. Mango fruit is considered in this paper. A comparative study on Faster R-CNN, YOLOv3 algorithms, and YOLOv4 algorithms, which are widely used in the field of object recognition in the past on various fruits and objects, was conducted to find the best model. The YOLOv4 algorithm was chosen as it was the best technique for mango fruit recognition based on the findings of the above comparative study. A real-time mango fruit detection method utilizing YOLOv4 deep learning algorithm is put forward. The YOLOv4 (You Only Look Once) model was developed under the CSPDarknet53 framework. Also, the number of mangoes in the image or frame was counted and displayed in images as well as videos. [ABSTRACT FROM AUTHOR]

China is the largest vegetable producer in the world, and vegetable production is more geographically concentrated in the Huang-Huai-Hai region and the Yangtze River Basin. There are significant challenges ahead for increasing the average yields of the vegetables in this region. The effects of a cultivation system, a mulched ridge with a double row (MRDR), were evaluated by using the 2ZBX-2A vegetable transplanter newly designed in this paper. The key parameters of the equipment were designed and optimized by using the human–computer interaction method and the discrete element method according to agronomy requirements. Compared with the traditional ridge (TR) system on two typical solanaceous vegetables (eggplant and capsicum), the uniformities of the plant spacing and the planting depth in the MRDR system were significantly improved. Finally, the fresh fruit yield in the MRDR system increased significantly (p < 0.05) by 40.8% and 35.3% compared with that in the TR system for eggplant and capsicum, respectively. In addition, the water use efficiency (WUE) was also 54.9~59.7% higher under the MRDR system than under the TR system. All the results indicate that the MRDR system has the potential to improve the yields and WUE of solanaceous vegetables in the Huang-Huai-Hai Plain of China. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fruit yield estimation is one of the challenging tasks using on-plant images to support smart farming and provide information about the product so that storage and export facility can be arranged. The detection and counting of on-plant fruits is a challenging task in complex vision. In this paper, we modify the intersection of union (IoU) in original Faster R-CNN (FR-CNN) for on-plant fruit detection. The modified IoU (MIoU) introduces good distance metric with the minimum area containing ground truth and predicted bounding box. Again, the MIoU pays extra attention to overlapping areas, which overcome the inefficiency of original R-CNN and enhance the detection accuracy. The proposed FR-CNN with MIoU achieved the correlation coefficient (R2) of mango, pomegranate, tomato, apple & orange are 0.98,0.92, 0.96, 0.98 & 0.95 respectively with the variation of imaging condition. In the same image sample, FR-CNN achieved the correlation coefficient (R2) of mango, pomegranate, tomato, apple & orange are 0.81,0.91,0.89, 0.90 & 0.92 respectively. So, the FR-CNN with MIoU enhances the detection accuracy compared to original FR-CNN. Again, the F1 score for apple, orange, tomato, pomegranate and mango is 0.9534, 0.9794, 0.9424, 0.9534 and mango 0.9383 respectively. In addition, the proposed method is efficient enough with less complex compared to state-of-art models for fruit detection. Again, the proposed methodology is evaluated using other state-of-art fruits datasets, namely ACFR dataset and KFuji RGB-DS dataset. The proposed methodology achieved F1 score of 0.9523 & 0.9432 for yield estimation of apple & mango of ACFR dataset and 0.8912 for KFuji RGB-DS dataset. [ABSTRACT FROM AUTHOR]