Fruit Growth Research Articles

Non-destructive weight prediction model of spherical fruits and vegetables using U-Net image segmentation and machine learning methods

Artificial intelligence has become increasingly prominent in agriculture and other fields. Prediction of body weight in animals and plants has been done by humans using many different methods and observations from the past to the present. Although there has been extensive research on predicting the live body weight of animals, weight prediction of vegetables and fruits is not widely. As spherical or round-shaped fruits and vegetables are sold by weighing in the fields, markets and greengrocers, it is important to make weight predictions. Based on this, a model was developed to predict the weight of fruits and vegetables such as watermelons, melons, apples, oranges and tomatoes with the data obtained from their images. The fruit and vegetable weights were predicted by regression models using data obtained from images segmented by the U-net architecture. Machine learning models such as Multi-Layer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Linear and Stochastic Gradient Descent (SDG) regression models were used for weight predictions. The most effective regression models are the Random Forest and Decision Trees models. For regression training, the best success rates were calculated as 0.9112 for watermelon, 0.9944 for apple, 0.9989 for tomato and 0.9996 for orange. In addition, the results were evaluated by comparing to the studies of weight prediction. The weight prediction model will help to sell round-shaped fruits and vegetables in the fields, markets and gardens using the weights predictions from the images. It is also a guideline for studies that follow the growth of fruit and vegetables according to their weight.

A SEPALLATA MADS-Box Transcription Factor, SlMBP21, Functions as a Negative Regulator of Flower Number and Fruit Yields in Tomato.

MADS-box transcription factors act as the crucial regulators in plant organ differentiation. Crop yields are highly influenced by the flower number and fruit growth. However, flower identification is a very complex biological process, which involves many cascade regulations. The molecular mechanisms underlying the genetic regulation of flower identification in cultivated plants, such as tomato, are intricate and require further exploration. In this study, we investigated the vital function of a SEPALLATA (SEP) MADS-box gene, SlMBP21, in tomato sympodial inflorescence meristem (SIM) development for the conversion from SIMs to floral meristems (FMs). SlMBP21 transcripts were primarily accumulated in young inflorescence meristem, flowers, sepals, and abscission zones. The Ailsa Craig (AC++) tomato plants with suppressed SlMBP21 mRNA levels using RNAi exhibited a large increase in flower number and fruit yields in addition to enlarged sepals and inhibited abscission zone development. Scanning electron microscopy (SEM) revealed that the maturation of inflorescence meristems (IMs) was repressed in SlMBP21-RNAi lines. RNA-seq and qRT-PCR analyses showed that numerous genes related to the flower development, plant hormone signal transduction, cell cycle, and cell proliferation et al. were dramatically changed in SlMBP21-RNAi lines. Yeast two-hybrid assay exhibited that SlMBP21 can respectively interact with SlCMB1, SFT, JOINTLESS, and MC, which play key roles in inflorescence meristems or FM development. In summary, our data demonstrate that SlMBP21 functions as a key regulator in SIM development and the conversion from SIMs to FMs, through interacting with other regulatory proteins to control the expression of related genes.

Optimization of Flowering and Fruit Development in Ber (Ziziphus mauritiana L.) cv. Umran using Fertilizers, Micronutrients, and Bioregulators

In order to investigate the effect of fertilizers, micronutrients and bioregulators on flowering and fruit growth and developments on ber (Ziziphus mauritiana L.) cv. Umran, an experiment was carried out during 2019/2020 cropping season at the Fruit Research Station, Imalia, Department of Horticulture JNKVV Jabalpur. The study used a factorial design using randomized complete block design (RCBD), with three replications and a total of twelve treatment combinations that included bioregulators, micronutrients, and fertilizers either separately or in various combinations. The application of treatment T4 [RDF (N:P:K)+Foliar spray of GA3 + NAA (50 ppm+50 ppm)] was found to be significantly superior as compared to other treatments for flowering parameter in respect to induce early flower bud formation, based on the results obtained in this study. It takes 22 days to induce the earliest flowering after spray, and it takes 8.67 days to achieve 50% flowering. The earliest date of flower bud initiation was found with the T11 [RDF (N:P:K)+ NPK -18:18:18 (1.0%) + ZnSO4 (0.5%) + Borax (0.3%)] which showed to be the best for fruit growth and development characteristics, including specific gravity (1.047), fruit weight (36.52 g), fruit volume (36.56 ml), fruit length (4.53 cm), and fruit diameter (3.50 cm).With regard to practically every fruit growth and development attribute, the treatment T10 was found to be the second best.

Potassium silicate and vinasse enhance biometric characteristics of perennial sweet pepper (Capsicum annuum) under greenhouse conditions

An effective strategy for enhancing fruit production continuity during extended sweet pepper season involves adopting innovative biostimulants such as potassium silicate (PS) and vinasse. Adjusting PS and vinasse concentrations are crucial for maintaining the balance between vegetative and fruit growth, particularly in sweet pepper with a shallow root system, to sustain fruiting over prolonged season. However, the interaction between PS and vinasse and the underlying physiological mechanisms that extend the sweet pepper season under greenhouse conditions remain unclear. This study aimed to investigate the impact of PS and vinasse treatments on the yield and biochemical constituents of perennial pepper plants cultivated under greenhouse conditions. For two consecutive seasons [2018/2019 and 2019/2020], pepper plants were sprayed with PS (0, 0.5, and 1 g/l) and drenched with vinasse (0, 1, 2, and 3 l/m3). To estimate the impact of PS and vinasse on the growth, yield, and biochemical constituents of pepper plants, fresh and dry biomass, potential fruit yield, and some biochemical constituents were evaluated. Results revealed that PS (0.5 g/l) coupled with vinasse (3 l/m3) generated the most remarkable enhancement, in terms of plant biomass, total leaf area, total yield, and fruit weight during both growing seasons. The implementation of vinasse at 3 l/m3 with PS at 0.5 and 1 g/l demonstrated the most pronounced augmentation in leaf contents (chlorophyll index, nitrogen and potassium), alongside improved fruit quality, including total soluble solid and ascorbic acid contents, of extended sweet pepper season. By implementing the optimal combination of PS and vinasse, growers can significantly enhance the biomass production while maintaining a balance in fruiting, thereby maximizing the prolonged fruit production of superior sweet pepper under greenhouse conditions.

Fine-grained recognition of bitter gourd maturity based on Improved YOLOv5-seg model

Bitter gourd, being perishable, requires timely harvesting. Delayed harvesting can result in a substantial reduction in fruit quality. while premature harvesting leads to underdeveloped fruit and decreased yields, the continuous flowering pattern in bitter gourd underscores the significance of accurately assessing fruit growth and ensuring timely harvesting for subsequent fruit setting and development. The current reliance on the experience of production personnel represents a substantial inefficiency. We present an improved real-time instance segmentation model based on YOLOv5-seg. The utilization of dynamic snake convolution enables the extraction of morphological features from the curved and elongated structure of bitter gourd. Diverse branch blocks enhance feature space diversity without inflating model size and inference time, contributing to improved recognition of expansion stages during bitter gourd growth. Additionally, the introduction of Focal-EIOU loss accurately locates the boundary box and mask, addressing sample imbalances in the L2 stage. Experimental results showcase remarkable accuracy rates of 99.3%, 93.8%, and 98.3% for L1, L2, and L3 stages using mAP@0.5. In comparison, our model outperforms other case segmentation models, excelling in both detection accuracy and inference speed. The improved YOLOv5-seg model demonstrates strong performance in fine-grained recognition of bitter gourd during the expansion stage. It efficiently segments bitter gourd in real-time under varying lighting and occlusion conditions, providing crucial maturity information. This model offers reliable insights for agricultural workers, facilitating precise harvesting decisions.

Comparative analysis of POD genes and their expression under multiple hormones in Pyrus bretschenedri

BackgroundClass III peroxidase (POD) enzymes play vital roles in plant development, hormone signaling, and stress responses. Despite extensive research on POD families in various plant species, the knowledge regarding the POD family in Chinese pear (Pyrus bretschenedri) is notably limited.ResultsWe systematically characterized 113 POD family genes, designated as PbPOD1 to PbPOD113 based on their chromosomal locations. Phylogenetic analysis categorized these genes into seven distinct subfamilies (I to VII). The segmental duplication events were identified as a prevalent mechanism driving the expansion of the POD gene family. Microsynteny analysis, involving comparisons with Pyrus bretschenedri, Fragaria vesca, Prunus avium, Prunus mume and Prunus persica, highlighted the conservation of duplicated POD regions and their persistence through purifying selection during the evolutionary process. The expression patterns of PbPOD genes were performed across various plant organs and diverse fruit development stages using transcriptomic data. Furthermore, we identified stress-related cis-acting elements within the promoters of PbPOD genes, underscoring their involvement in hormonal and environmental stress responses. Notably, qRT-PCR analyses revealed distinctive expression patterns of PbPOD genes in response to melatonin (MEL), salicylic acid (SA), abscisic acid (ABA), and methyl jasmonate (MeJA), reflecting their responsiveness to abiotic stress and their role in fruit growth and development.ConclusionsIn this study, we investigated the potential functions and evolutionary dynamics of PbPOD genes in Pyrus bretschenedri, positioning them as promising candidates for further research and valuable indicators for enhancing fruit quality through molecular breeding strategies.

Mitigating Fruit Cracking and Premature Dropping in Bael (Aegle marmelos) cv. NB-9 under Subtropical Conditions: Evaluating the Effects of Bio-formulations and Boric Acid

The present investigation entitled “Mitigating Fruit Cracking and Premature Dropping in Bael (Aegle marmelos) cv. NB-9 under Subtropical Conditions: Evaluating the Effects of Bio-formulations and Boric Acid” was carried out during the year 2022-2023 at Main Experimental Station, Department of Fruit Science, College of Horticulture & Forestry, A.N.D.U.A.&T., Narendra Nagar (Kumarganj), Ayodhya, Uttar Pradesh, India (Lat 26.540849° Long 81.830421°). Application of various bio-formulations and boric acid with eco-friendly materials to study the various vegetative properties, cracking, fruit drop and effect on the vegetative growth of fruit. The experiment was laid down in randomized block design (RBD) with (12) treatments and (03) replications. The experiment consists of 12 treatments including control, T1 (Control), T2 (FYM 50 Kg), T3 (FYM 100 Kg), T4 (Boric acid 200g), T5 (Boric acid 400g), T6 (Biofertilizer 250g/plant [100g Azotobacter + 100g Biovita + 50g PSB]), T7 (FYM 50Kg + Boric acid 200g), T8 (FYM 50Kg + Boric acid 400g), T9 (FYM 50Kg +Biofertilizer 250g/plant [100g Azotobacter + 100g Biovita + 50g PSB]), T10 (FYM 100Kg + Boric acid 200g), T11 (FYM 100 Kg + Boric acid 400g) and T12 (FYM 100Kg +Biofertilizer 250g/plant [100g Azotobacter + 100g Biovita + 50g PSB]) was used for this study. The results revealed that treatment T7 outperformed the rest with minimum fruit cracking (1.53%), fruit drop (76.20%), the maximum number of flowers per plant (171.19), day-to-flowering (31.38) and yield (181.69 q/ha) was most effective in a vegetative quality and yield point of view. Thus, the treatment combination T7 is therefore recommended for application to Bael trees in subtropical conditions to obtain high yields with better quality fruits.

Predicting fruit set based on the fruit growth rate model with vision systems

Predicting fruit set based on the fruit growth rate model with vision systems

A computer vision approach for estimating fruit growth rate in orchards

A computer vision approach for estimating fruit growth rate in orchards

IoT network to water management in an irrigation district: Study case in Colombia

One of the critical factors for crop efficiency is irrigation optimization. This optimization uses the knowledge of water dynamics in the soil to develop efficient irrigation programming. Previously, we described a prototype of a system that used soil moisture sensors to control the frequency and duration of the irrigation of a Limon Tahiti, showing a reduction in water and energy use. This paper describes IoT's use for optimizing irrigation for ten crops of socioeconomic importance in Colombia. Besides the soil moisture, the system also collects information on fruit growth and climatic variables. Results evidence a reduction in water consumption and several benefits for the crops, proving especially beneficial for small-scale agriculture.

Response of Fruit Growth and Quality to the Spray of Plant Growth Regulator in Yellow-fleshed Kiwifruits

Response of Fruit Growth and Quality to the Spray of Plant Growth Regulator in Yellow-fleshed Kiwifruits

Current Advances in the Functional Genes of Edible and Medicinal Fungi: Research Techniques, Functional Analysis, and Prospects.

Functional genes encode various biological functions required for the life activities of organisms. By analyzing the functional genes of edible and medicinal fungi, varieties of edible and medicinal fungi can be improved to enhance their agronomic traits, growth rates, and ability to withstand adversity, thereby increasing yield and quality and promoting industrial development. With the rapid development of functional gene research technology and the publication of many whole-genome sequences of edible and medicinal fungi, genes related to important biological traits have been mined, located, and functionally analyzed. This paper summarizes the advantages and disadvantages of different functional gene research techniques and application examples for edible and medicinal fungi; systematically reviews the research progress of functional genes of edible and medicinal fungi in biological processes such as mating type, mycelium and fruit growth and development, substrate utilization and nutrient transport, environmental response, and the synthesis and regulation of important active substances; and proposes future research directions for functional gene research for edible and medicinal fungi. The overall aim of this study was to provide a valuable reference for further promoting the molecular breeding of edible and medicinal fungi with high yield and quality and to promote the wide application of edible and medicinal fungi products in food, medicine, and industry.

Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning

Accurate and real-time monitoring of soil moisture content (SMC) is of utmost importance for effective field irrigation and maximizing crop water productivity. However, a comprehensive investigation into the inversion study for determining suitable combinations of unmanned aerial vehicle (UAV) image features and enhancing the precision of SMC model prediction has yet to be fully validated within a kiwifruit orchard setting. This study addresses this gap by employing a pre-processing method and an optimal band combination algorithm to assess the impact of various combinations of kiwifruit canopy reflectance and fraction vegetation coverage (FVC) features on the sensitivity of root-zone SMC. Furthermore, an optimal ensemble learning (EL) framework was developed to monitor SMC at various root-zone depths (0–10 cm [SMC10], 0–20 cm [SMC20], 0–30 cm [SMC30], 0–40 cm [SMC40], 0–50 cm [SMC50], 0–60 cm [SMC60]). The key findings of this research highlight the successful derivation of 10 wavebands and FVC features, exhibiting a strong correlation with SMC at different root depths. The gradient boosting (GBDT) model demonstrated the exceptional accuracy in estimating SMC10, with an impressive R2 value of 0.963 ± 0.030 and low RMSE values of 0.238 ± 0.111. Similarly, the eXtreme Gradient Boosting (XGBoost) model outperformed in estimating SMC20 to SMC60, with R2 and RMSE values of 0.963 ± 0.024 and 0.117 ± 0.053, respectively. Additionally, the utilization of the optimal EL model allows for digital mapping of SMC at different depths across fruit growth stages, showcasing superior adaptability for SMC30 to SMC60 (with R2 and RMSE of 0.782 ± 0.090 and 0.037 ± 0.011) compared to SMC10 and SMC20 (with R2 and RMSE of 0.765 ± 0.097 and 0.056 ± 0.024). These results underscore the potential of the EL estimation framework in characterizing the spatial distribution of root-zone SMC at the individual kiwifruit plant level.

Effect of calcium on fruit quality: A review

Calcium (Ca) plays a crucial role as a nutrient influencing the ripening, post-harvest duration, and quality of fruits. Its impact on the cell wall and its function as a secondary messenger at the cellular level underscore its significance. While recently there has been an increase in studies examining the effects of Ca on fruit quality, there remains a need to consolidate and expand the literature on pre-harvest and post-harvest applications of Ca concerning the physical-chemical properties of fruits. This review aims to compile information on the mechanisms of Ca absorption by plants, as well as the interaction of Ca with the cell wall in fruit development and growth; the review also aims to synthesize literature on the effects of calcium on the physical and chemical properties of fruits that ultimately influences their quality. The review considers a comprehensive analysis of studies published in reputable scientific publishers (Elsevier, Springer, Frontiers, Wiley, MDPI, Hindawi, SciELO) over the last ten years, encompassing various relevant topics. Calcium proves effective in retarding the loss of firmness in fruits, increasing their mass, mitigating mass loss during storage, and extending postharvest life, thereby enhancing marketability. Furthermore, Ca demonstrates a role in decreasing the activity of enzymes responsible for cell wall degradation. Additionally, it reduces ethylene production in fruits, delaying the climacteric peak and reducing its intensity. Its application results in delayed color changes in fruits. For soluble solids, Ca diminishes sugar values and postpones their peak during the post-harvest period while maintaining high total acidity values. Notably, Ca applications contribute to a decreased incidence of certain physiological disorders.

Effect of air temperature on each fruit growth and ripening stage of strawberry ‘Koiminori’

Effect of air temperature on each fruit growth and ripening stage of strawberry ‘Koiminori’

A computer vision system for apple fruit sizing by means of low-cost depth camera and neural network application

AbstractFruit size is crucial for growers as it influences consumer willingness to buy and the price of the fruit. Fruit size and growth along the seasons are two parameters that can lead to more precise orchard management favoring production sustainability. In this study, a Python-based computer vision system (CVS) for sizing apples directly on the tree was developed to ease fruit sizing tasks. The system is made of a consumer-grade depth camera and was tested at two distances among 17 timings throughout the season, in a Fuji apple orchard. The CVS exploited a specifically trained YOLOv5 detection algorithm, a circle detection algorithm, and a trigonometric approach based on depth information to size the fruits. Comparisons with standard-trained YOLOv5 models and with spherical objects were carried out. The algorithm showed good fruit detection and circle detection performance, with a sizing rate of 92%. Good correlations (r > 0.8) between estimated and actual fruit size were found. The sizing performance showed an overall mean error (mE) and RMSE of + 5.7 mm (9%) and 10 mm (15%). The best results of mE were always found at 1.0 m, compared to 1.5 m. Key factors for the presented methodology were: the fruit detectors customization; the HoughCircle parameters adaptability to object size, camera distance, and color; and the issue of field natural illumination. The study also highlighted the uncertainty of human operators in the reference data collection (5–6%) and the effect of random subsampling on the statistical analysis of fruit size estimation. Despite the high error values, the CVS shows potential for fruit sizing at the orchard scale. Future research will focus on improving and testing the CVS on a large scale, as well as investigating other image analysis methods and the ability to estimate fruit growth.

A geometric modelling approach to estimate apple fruit size by means of LiDAR 3D point clouds

ABSTRACT Remote sensing in agriculture aims to search new methods to monitor fruit at the tree, thus improving the estimation of yield-related variables. Light detection and range (LiDAR) scanning was introduced to obtain geometric and radiometric information from fruit surfaces by means of 3D point clouds. A geometric model to estimate apple size by means of segmented 3D point cloud of fruit is proposed in the present study. The model consists in the approximation of 3D point clouds to a reference shape given by 2D Fourier series expansion. Each point cloud was approximated to its reference shape using an iterative error minimization routine. The geometric model was applied to laboratory and field data of spheres and apples during fruit growth, ranging from 60 to 151 days after full bloom (DAFB). An overall RMSE between measured and predicted fruit radius of 20.1, 76.8, and 119.1% was found for the geometric model, mean, and maximum Euclidean distance approaches, respectively, including all studied growth periods in field conditions. Moreover, the linear regression on measured and predicted values showed considerably improved coefficient of determination ( R 2 ) of the geometric model in comparison to Euclidean distance calculations with R 2 values of 0.76 and 0.49 for laboratory and field scanned apples, respectively. The data processing method enables fruit monitoring and their application of terrestrial LiDAR sensing in precise orchard management.

The CELL NUMBER REGULATOR FW2.2 protein regulates cell-to-cell communication in tomato by modulating callose deposition at plasmodesmata.

FW2.2 (standing for FRUIT WEIGHT 2.2), the founding member of the CELL NUMBER REGULATOR (CNR) gene family, was the first cloned gene underlying a quantitative trait locus (QTL) governing fruit size and weight in tomato (Solanum lycopersicum). However, despite this discovery over 20 yr ago, the molecular mechanisms by which FW2.2 negatively regulates cell division during fruit growth remain undeciphered. In the present study, we confirmed that FW2.2 is a membrane-anchored protein whose N- and C-terminal ends face the apoplast. We unexpectedly found that FW2.2 is located at plasmodesmata (PD). FW2.2 participates in the spatiotemporal regulation of callose deposition at PD and belongs to a protein complex which encompasses callose synthases. These results suggest that FW2.2 has a regulatory role in cell-to-cell communication by modulating PD transport capacity and trafficking of signaling molecules during fruit development.

Metabolic analyses and different expression of gene related to flavonoid synthesis pathway in three different color cultivars of grape (Vitis vinifera L.)

In this study, Shine Muscat (YG), Gaoqiansui (GQS) and Moldova (ME) were used to study the changes of anthocyanin and related genes expression in grape skin during fruit growth and development. CHS, F3’H, ANS and UFGT were the main genes with relatively high expression levels. Significant differences were observed in the expressions of CHS, F3’H, ANS, and UFGT among YG, GQS, ME in most fruit developmental stages. Large differences were discovered in the anthocyanin contents and components among three grape cultivars. During fruit development YG only contained trace anthocyanins. Peonidin, malvidin and cyanidin derivates are the main components in GQS, while the main components of ME are malvidin, petunidin, and delphinidin. Almost all the genes tested were positively correlated with anthocyanins between GQS and ME but irrelevant in YG during fruit development. High expression of CHS, F3’H, ANS, and UFGT and other genes probably promote the synthesis of anthocyanins. Overall CHS, F3’H, ANS, and UFGT were proposed as prominent candidate genes for anthocyanin synthesis in grape skin. This study provides reference for further understanding the physiological and biochemical mechanisms of anthocyanin synthesis in grape fruits which could be better for breeding programs.

Multi-nutrient fertilization-based analysis of fruit quality and mineral element composition during fruit development in Merlot wine grapevine1

Mineral elements play a crucial role in plant growth and development. Ensuring the proper supply of these elements in soil and maintaining an optimum range within plants is necessary for achieving optimal fruit yield and quality. Unfortunately, the application of NPKCaMg fertilizers to fruit trees is often either insufficient or excessive, leading to both environmental degradation and reduced fruit yield and quality. To assess the impact of different fertilizers on the biological-related traits of grape fruit and its response to nutritional elements, Merlot grapevines were subjected to multi-nutrient fertilization over four consecutive growing seasons from 2018 to 2021 in Penglai District, Yantai, China. Principal component analysis revealed that T11 treatment, consisting of N3P3K1Ca2Mg4, was the most suitable fertilizer type and application design for this experiment. The application of T11 resulted in a significant decrease (24.29%–35.20%) in fertilizer usage, and it resulted in an increase in several important traits such as 100 grain weight (HGW), number of seeds (SN), total soluble solids (TSS), total seed phenols (SP), seed flavanols (SFI), and seed tannins (ST) by 3.28%–12.84%, 3.76%–20.03%, 1.11%–14.95%, 2.16%–23.69%, 11.00%–32.78%, 1.07%–23.35%, respectively, compared to T14 (N4P2K3Ca1Mg4), T16 (N4P4K1Ca3Mg2), T13 (N4P1K4Ca2Mg3), and T15 (N4P3K2Ca4Mg1) treatments. Flowering and fruiting behavior exhibited a considerable demand for NPK, with a higher requirement for K and B during fruit growth and development compared to the other mineral elements. Excessive K in soil was found to enhance the competitive inhibition of Ca uptake by Merlot grapevines. The optimum ranges of mineral element content for total peel phenols (PP), peel flavanols (PFI), total peel flavonoids (PFD), total seed phenols (SP), and seed tannins (ST) were primarily influenced by grape variety and nutritional analysis method. In conclusion, the careful selection of NPKCaMg fertilizer and its meticulous application to soil at an optimum range of mineral elements are critical for grapevine growth and development.