Style length and flower morphology of three eggplant (Solanum melongena L.) cultivars from Iran affected by fruit load

<p>A common feature of eggplant is its heterostyly. Long-style flowers bear fruits whereas short style ones fail to do so. Heterostyly is influenced by some factors such as genotype, climatic conditions and fruit load. In this study three eggplant cultivars from Iran were cultivated under greenhouse condition. The influence of presence of fruit (two fruits and four fruits) or absence of that on style length and some other flower morphological was studied in three positions of single, basal and additional. The presence of fruit, specially four fruits reduced style length, stigma width as well as mass of flower, pistil and stigma compared to the control in all times during fruit growth, and after fruit harvest they increased again. Fruit load didn’t affect the number of stamen and stamen length. These effects were observed in all three positons of single, basal and additional flowers of all three cultivars. Generally this study showed that fruit load has decreasing effect on style length and size of flowers forming after fruit setting, which reversed after fruit harvesting.</p>

Estimation of Fruit Load in Australian Mango Orchards Using Machine Vision

The performance of a multi-view machine vision method was documented at an orchard level, relative to packhouse count. High repeatability was achieved in night-time imaging, with an absolute percentage error of 2% or less. Canopy architecture impacted performance, with reasonable estimates achieved on hedge, single leader and conventional systems (3.4, 5.0, and 8.2 average percentage error, respectively) while fruit load of trellised orchards was over-estimated (at 25.2 average percentage error). Yield estimations were made for multiple orchards via: (i) human count of fruit load on ~5% of trees (FARM), (ii) human count of 18 trees randomly selected within three NDVI stratifications (CAL), (iii) multi-view counts (MV-Raw) and (iv) multi-view corrected for occluded fruit using manual counts of CAL trees (MV-CAL). Across the nine orchards for which results for all methods were available, the FARM, CAL, MV-Raw and MV-CAL methods achieved an average percentage error on packhouse counts of 26, 13, 11 and 17%, with SD of 11, 8, 11 and 9%, respectively, in the 2019–2020 season. The absolute percentage error of the MV-Raw estimates was 10% or less in 15 of the 20 orchards assessed. Greater error in load estimation occurred in the 2020–2021 season due to the time-spread of flowering. Use cases for the tree level data on fruit load was explored in context of fruit load density maps to inform early harvesting and to interpret crop damage, and tree frequency distributions based on fruit load per tree.

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.

Effects of Fruit Load on Sugar/Acid Quality and Puffiness of Delayed-Harvest Citrus

Delayed harvesting technology is believed to improve the citrus fruit flavor, but improper tree fruit load under delayed harvest might cause puffiness and reduce fruit quality. In order to find out an optimum tree fruit load level to obtain better flavor quality as well as reduce puffiness in delayed-harvest citrus under protected cultivation, experiments were conducted in the present study between 2019 and 2020 to determine the effect of different fruit loads and fruit-bearing per single branch on the soluble sugars and organic acids metabolism in the peel and flesh, the anatomical structure of the matured fruit peel, and fruit texture-related indexes. The results suggested significant negative correlations between leaf N level and flesh sucrose and glucose contents, and between branch P level and flesh citric acid contents; no significant correlation between NPK levels and flesh texture; relatively lower leaf N and branch P under relatively higher load can increase flesh sucrose and glucose accumulation and slow down citric acid degradation to the greater extent, thus optimizing the sugar/acid ratio of fruits during delayed harvest. The lignification of parenchyma cells closely around peel secretory cavities due to ascorbic acid deficiency might be the primary cause for puffiness under low-load treatments.

Vegetative and Reproductive Response to Fruit Load in Two Jojoba (Simmondsia chinensis) Cultivars

Jojoba (Simmondsia chinensis) is a wax crop cultivated mainly in arid and semi-arid regions. This crop has been described as an alternate-bearing plant, meaning that it has a high-yield year (“on-year”) followed by a low-yield year (“off-year”). We investigated the effect of fruit load on jojoba’s vegetative and reproductive development. For two consecutive years, we experimented with two high-yielding cultivars—Benzioni and Hazerim—which had opposite fruit loads, i.e., one was under an on-year load, while the other was under an off-year load simultaneously. We found that removing the developing fruit from the shoot during an off-year promotes further vegetative growth in the same year, whereas in an on-year, this action has no effect. Moreover, after fruit removal in an on-year, there was a delay in vegetative growth renewal in the consecutive year, suggesting that the beginning of the growing period is dependent on the previous year’s yield load. We found that seed development in the 2018 season started a month earlier than in the 2017 season in both cultivars, regardless of fruit load. This early development was associated with higher wax content in the seeds. Hence, the wax accumulation rate, as a percentage of dry weight, was affected by year and not by fruit load. However, on-year seeds stopped growing earlier than off-year seeds, resulting in smaller seeds and an overall lower amount of wax per seed.

Effects of container size and fruit load intensity on tomato under salt stress

&lt;p&gt;Container size and fruit load intensity are two common factors manipulated to regulate plant growth and development. As saline water is increasingly used for irrigation in arid and semi-arid regions, it is important to study effects of container size and fruit load intensity on tomato in both aboveground and belowground parts under salt stress. The experiment was conducted in a net house located in Sede Boqer Campus, Israel. Containers of four sizes (8-, 28-, 48-, and 200L with the same depth but vary in diameters), two salinity levels (1.5- and 7.5 dS m&lt;sup&gt;&amp;#8722;1&lt;/sup&gt;) and two crop load intensities (0% and 100%) were applied. Gas exchange parameters (i.e., stomatal conductance and CO&lt;sub&gt;2 &lt;/sub&gt;assimilation rate), plant growth parameters (i.e., plant height and stem diameter), and root development were monitored periodically. Plant biomass and various root traits were measured at harvest. For aboveground part, results revealed that container size and salinity level significantly influenced gas exchange performance while fruit load intensity had no significant effect. Plants grown in larger containers without salt stress had higher stomatal conductance and CO&lt;sub&gt;2 &lt;/sub&gt;assimilation rate. Plant height and stem diameter were significantly greater in plants grown in 200L than those in other containers despite salinity and fruit load levels. Moreover, plants grown in 200L containers exhibited significant increase of 56.3%, 152.9%, and 174.9% respectively in yield compared with those grown in 48-, 28- and 8L under salt stress. The increase magnitudes were greater when there was no salt stress: 109.0%, 430.8%, and 454.0% respectively. For belowground parts, increased container size leads to increased rooting depth. Besides, Minirhizotron data showed that in 200L containers, plants grown under low salinity without fruit developed the greatest total root length. More detailed root data will be presented.&amp;#160; It is concluded that container size has a pronounced effect on physiological behaviours of tomato plants. Therefore, properly increasing container size can alleviate yield reduction under saline irrigation.&lt;/p&gt;

Genetic mechanisms associated with floral initiation and the repressive effect of fruit on flowering in apple (Malus x domestica Borkh)

Many apple cultivars are subject to biennial fluctuations in flowering and fruiting. It is believed that this phenomenon is caused by a repressive effect of developing fruit on the initiation of flowers in the apex of proximal bourse shoots. However, the genetic pathways of floral initiation are incompletely described in apple, and the biological nature of floral repression by fruit is currently unknown. In this study, we characterized the transcriptional landscape of bourse shoot apices in the biennial cultivar, ’Honeycrisp’, during the period of floral initiation, in trees bearing a high fruit load and in trees without fruit. Trees with high fruit load produced almost exclusively vegetative growth in the subsequent year, whereas the trees without fruit produced flowers on the majority of the potential flowering nodes. Using RNA-based sequence data, we documented gene expression at high resolution, identifying >11,000 transcripts that had not been previously annotated, and characterized expression profiles associated with vegetative growth and flowering. We also conducted a census of genes related to known flowering genes, organized the phylogenetic and syntenic relationships of these genes, and compared expression among homeologs. Several genes closely related to AP1, FT, FUL, LFY, and SPLs were more strongly expressed in apices from non-bearing, floral-determined trees, consistent with their presumed floral-promotive roles. In contrast, a homolog of TFL1 exhibited strong and persistent up-regulation only in apices from bearing, vegetative-determined trees, suggesting a role in floral repression. Additionally, we identified four GIBBERELLIC ACID (GA) 2 OXIDASE genes that were expressed to relatively high levels in apices from bearing trees. These results define the flowering-related transcriptional landscape in apple, and strongly support previous studies implicating both gibberellins and TFL1 as key components in repression of flowering by fruit.

Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning

Machine vision from ground vehicles is being used for estimation of fruit load on trees, but a correction is required for occlusion by foliage or other fruits. This requires a manually estimated factor (the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruits. Several image features, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest count of fruit per tree. A R2 of 0.98 (n = 98 trees) was achieved for the correlation of fruit count predicted by a Random forest model and the ground truth fruit count, compared to a R2 of 0.68 for the reference method. Error on prediction of whole orchard (880 trees) fruit load compared to packhouse count was 1.6% for the MLP model and 13.6% for the reference method. However, the performance of these models on data of another season was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model.

The role of auxin and sugar signaling in dominance inhibition of inflorescence growth by fruit load

ABSTRACTDominance inhibition of shoot growth by fruit load is a major factor that regulates shoot architecture and limits yield in agriculture and horticulture crops. In annual plants, the inhibition of inflorescence growth by fruit load occurs at a late stage of inflorescence development termed the end of flowering transition. Physiological studies show that this transition is mediated by production and export of auxin from developing fruits in close proximity to the inflorescence apex. In the meristem, cessation of inflorescence growth is controlled in part by the age dependent pathway, which regulates the timing of arrest. Here, results show that the end of flowering transition is a two-step process in which the first stage is characterized by a cessation of inflorescence growth, while immature fruit continue to develop. At this stage, dominance inhibition of inflorescence growth by fruit load correlates with a selective dampening of auxin transport in the apical region of the stem. Subsequently, an increase in auxin response in the vascular tissues of the apical stem where developing fruits are attached marks the second stage for the end of flowering transition. Similar to the vegetative and floral transition, the end of flowering transition correlates with a change in sugar signaling and metabolism in the inflorescence apex. Taken together, our results suggest that during the end of flowering transition, dominance inhibition of inflorescence shoot growth by fruit load is mediated by auxin and sugar signaling.One-sentence summaryDominance inhibition of inflorescence shoot growth by fruit load is involves auxin and sugar signaling during the end of flowering transition.

Attempting to Estimate the Unseen – Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision With Deep Learning

Imaging systems mounted to ground vehicles are used to image fruit tree canopies for estimation of fruit load, but frequently need correction for fruit occluded by branches, foliage or other fruits. This can be achieved using an orchard &lsquo;occlusion factor&rsquo;, estimated from a manual count of fruit load on a sample of trees (referred to as the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruit. Five approaches to correct for occluded fruit based on canopy images were compared using data of three mango orchards in two seasons. However, no attribute correlates to the number of hidden fruit were identified. Several image features obtained through segmentation of fruit and canopy areas, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest fruit count on trees. The supervised machine learning methods for direct estimation of fruit load per tree delivered an improved prediction outcome over the reference method for data of the season/orchard from which training data was acquired. For a set of 2017 season tree images (n=98 trees), a R2 of 0.98 was achieved for the correlation of the number of fruits predicted by a Random forest model and the ground truth fruit count on the trees, compared to a R2 of 0.68 for the reference method. The best prediction of whole orchard (n = 880 trees) fruit load, in the season of the training data, was achieved by the MLP model, with an error to packhouse count of 1.6% compared to the reference method error of 13.6%. However, the performance of these models on new season data (test set images) was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model. This outcome was attributed to variability in tree architecture and foliage density between seasons and between orchards, such that the characters of the canopy visible from the interrow that relate to the proportion of hidden fruit are not consistent. Training of these models across several seasons and orchards is recommended.