Fruit Position Research Articles

Characterization of Prickly Pear plantations in semi-arid zones! A green revolution in East Algeria: Case of the Tébessa region

The study carried out on the prickly pear shrub, in three potential production sites belonging to the commune of Ain Zerga district of Tébessa in the extreme east of the Algerian arid zone. The aim of this study was to try to interpret the main characteristics and sources of variability of this shrub of multiple interests. Carried out in 2023 on orchards planted with adult Opuntia ficus indica, developed in 45 orchards divided into 15 per site located in three localities, namely Henchir Lahdid, El Guefaifia, and Ouled Mebarek. The trees are grown extensively, with production starting in the last week of May. The cactus shrub reaches an equivalent production (10.23) kg and a production of 11,137.65kg/ha. At the time of the commercial harvest, indicated by the change in skin color, we carried out a random collection of 05 fruits per orchard, i.e. 75 fruits per site (225 fruits); these fruits were evaluated by various measurements. Shrub-specific variables such as fruit position in the canopy, cladode length, width, density, and inter-line spacing, number and weight of fruits per tree and per cladode, and annual productivity were studied. These variables per cactus shrub and fruit changed significantly as a function of climatic and edaphic environmental conditions, i.e. site and altitude, while the number of seeds/fruits was conditioned by volume. Fruit weight varied greatly within the same tree, influenced by the number of fruits per tree and the surface area of the cladodes; this variability decreased for cladodes bearing more than six fruits. Interception of climatic conditions (light) and cladode dry weight were the sources of variability in fruit weight, whereas Opuntia plant/hectare density had no effect on fruit quality.

The analysis of vegetable and fruit export competitiveness of the Republic of Srpska

The aim of this research is an analysis of the Republic of Srpska's export competitiveness in vegetables and fruit on international markets. Two main evaluation indices were used, namely the trade competitiveness (TC) index and the revealed comparative advantage (RCA) index to measure and analyze the international position of vegetables and fruit during 2015 – 2022. Both indicators were calculated based on statistical data on foreign trade in vegetables and fruit between the Republic of Srpska and the world, EU, and CEFTA countries, and analyzed using descriptive statistics. Vegetables and fruit participate with 7.17% and 15.41% in the total export of the Republic of Srpska’s agri–food sector. Analysis of the export structure of this sector showed an average share of vegetables and fruit of 12.04% and 9.43% in the EU market, i.e., 1.63% and 9.65% in the CEFTA market. The results indicate strong export competitiveness of the Republic of Srpska in vegetables concerning the world and EU markets, but weak export competitiveness in relation with the CEFTA countries. The Republic of Srpska recorded strong export competitiveness in fruit on the world and CEFTA market, while poor export competitiveness in the EU market. Within these two product groups on the world market, cucumbers, apples, pears, and plums achieved strong export competitiveness. Cucumbers and plums were two leading products regarding strong export competitiveness On the EU market, while apples and pears were on the CEFTA market. Considering the favourable agroecological conditions for the production of vegetables and fruit, the Republic of Srpska should continue to promote production and foreign trade in these traditional products and improve their competitiveness on international markets.

Genomes of autotetraploid wild and cultivated Ziziphus mauritiana reveal polyploid evolution and crop domestication.

Indian jujube (Ziziphus mauritiana) holds a prominent position in the global fruit and pharmaceutical markets. Here, we report the assemblies of haplotype-resolved, telomere-to-telomere genomes of autotetraploid wild and cultivated Indian jujube plants using a two-stage assembly strategy. The generation of these genomes permitted in-depth investigations into the divergence and evolutionary history of this important fruit crop. Using a graph-based pan-genome constructed from eight monoploid genomes, we identified structural variation (SV)-FST hotspots and SV hotspots. Gap-free genomes provide a means to obtain a global view of centromere structures. We identified presence-absence variation-related genes in four monoploid genomes (cI, cIII, wI, and wIII) and resequencing populations. We also present the population structure and domestication trajectory of the Indian jujube based on the resequencing of 73 wild and cultivated accessions. Metabolomic and transcriptomic analyses of mature fruits of wild and cultivated accessions unveiled the genetic basis underlying loss of fruit astringency during domestication of Indian jujube. This study reveals mechanisms underlying the divergence, evolution, and domestication of the autotetraploid Indian jujube and provides rich and reliable genetic resources for future research.

Development of a new winter squash cultivar ‘Houchouko 1’ with short internodes and stable fruit position

Development of a new winter squash cultivar ‘Houchouko 1’ with short internodes and stable fruit position

Genetic Diversity Assessment of Brinjal Germplasm for Varietal Improvement

Forty-one (41) brinjal (Solanum melongena L.) germplasm of were evaluated at the field of Plant Genetic Resources Centre, BARI, Gazipur to identify the salient features and to assess genetic divergence of germplasm in the time of rabi season 2021-22. Distinct variation was observed in qualitative characters among the germplasm except two characters i.e. fruit cross section and fruit position. For the characters length breath ratio and calyx prickles of fruit, the topmost variation was found. In case of quantitative characters, variation was noticed maximum in the length of fruit (CV-43.79%), afterwards individual weight of fruit (CV-32.91%) and yield plant-1 (CV-30.12%). The highest number of germplasm was dispersed into cluster I. Among the clusters, cluster II and cluster III were occupied by ten germplasm, respectively. Cluster mean value was noted maximum in cluster II for the characters days required for 50% flowering (67.90%), plant height (97.97 cm), individual fruit weight (107.35 g), and yield plant-1 (2.51%). Among the principal components, 27.28% of the total variation interpreted by the first PC and the second PC clarified 22.36% of the variation. In the biplot, germplasm NF-15, NF-20, NF-13, NF-7, NF-4, NF-25, NF-16, and NF-2 etc. were independently far away from the studied germplasm by keeping a distance from the centroid. These results displayed distinctiveness and divergence of germplasm as regards of the studied traits. Populations with high values for the first eigen vectors are individual fruit weight (0.642), yield plant-1 (0.564), and width of fruit (0.313), these characters were the utmost significant contributors towards the diversity of germplasm in PC1. The germplasm NF-4 (3.45 kg), NF-30 (3.38 kg), and NF-25 (3.08 kg) were higher yielders among the tested germplasm and may be used for future varietal improvement of brinjal. J. of Sci. and Tech. Res. 5(1): 21-29, 2023

3D positioning of Camellia oleifera fruit-grabbing points for robotic harvesting

Camellia oleifera is an oilseed crop with high economic value. The short optimum harvest period and high labour costs of C. oleifera harvesting have prompted research on intelligent robotic harvesting. This study focused on the determination of grabbing points for the robotic harvesting of C. oleifera fruits, providing a basis for the decision making of the fruit-picking robot. A relatively simple 2D convolutional neural network (CNN) and stereoscopic vision replaced the complex 3D CNN to realise the 3D positioning of the fruit. Apple datasets were used for the pretraining of the model and knowledge transfer, which shared a certain degree of similarity to C. oleifera fruit. In addition, a fully automatic coordinate conversion method has been proposed to transform the fruit position information in the image into its 3D position in the robot coordinate system. Results showed that the You Only Look Once (YOLO)v8x model trained using 1012 annotated samples achieved the highest performance for fruit detection, with mAP50 of 0.96 on the testing dataset. With knowledge transfer based on the apple datasets, YOLOv8x using few-shot learning realised a testing mAP50 of 0.95, reducing manual annotation. Moreover, the error in the 3D coordinate calculation was lower than 2.1 cm on the three axes. The proposed method provides the 3D coordinates of the grabbing point for the target fruit in the robot coordinate system, which can be transferred directly to the robot control system to execute fruit-picking actions. This dataset was published online to reproduce the results of this study.

Fruit Position, Light Exposure and Fruit Surface Temperature Affect Colour Expression in a Dark-Red Apple Cultivar

This study aimed to evaluate the effects of fruit position, light exposure and fruit surface temperature (FST) on apple fruit colour development and fruit quality at harvest, including sunburn damage severity. This was achieved by undertaking two experiments in a high-density planting of the dark-red apple ANABP 01 in Tatura, Australia. In the 2020–2021 growing season an experiment was conducted to draw relationships between fruit position and fruit quality parameters. Here, sample fruit position and level of light exposure were respectively determined using a static LiDAR system and a portable quantum photosynthetically active radiation (PAR) sensor. At harvest the sample fruit were analysed for percentage red colour coverage, objective colour parameters (L*, a*, b*, hue angle and chroma), sunburn damage, fruit diameter (FD), soluble solids concentration (SSC), flesh firmness (FF) and starch pattern index (SPI). A second experiment was conducted in the 2021–2022 growing season and focused on how fruit shading, light exposure and the removal of ultraviolet (UV) radiation affected the FST, colour development and harvest fruit quality. Five treatments were distributed among sample fruit: fully shaded with aluminium umbrellas, shaded for one month and then exposed to sunlight until harvest, exposed for one month and then shaded until harvest, covered with a longpass UV filter and a control treatment. The development of colour in this dark-red apple cultivar was highly responsive to aspects of fruit position, and the intensity and quality of light exposure. The best-coloured fruit were exposed to higher quantities of PAR, exposed to both PAR and UV radiation simultaneously and located higher in the tree canopy. Fruit that were fully exposed to PAR and achieved better colour development also displayed higher FST and sunburn damage severity.

Application of Stereo Vision to Control the Movement of the Robot Arm Towards the Position of Red Chilies

The trend of decreasing young workers in the agricultural sector needs to be anticipated by developing intelligent machines known as agricultural robots. This research aims to apply a stereo vision system to control the movement of the robot's grip towards the 3D position of the red chili fruit. The stereo vision system installed on the robot waist (joint-2) is used to capture plant images and process them using HSV masking filters and triangulation principal to obtain the 3D center point position of the fruit. The robot joint movement is calculated using geometric based inverse kinematics. The research results show that the average accuracy of the stereo vision system is 93.9 %. The average grip positioning accuracy is 95.6 % to the actual chili fruit position and 98.5 % to the stereo vision calculation value. The average stability of the stereo vision values is 99.5 %, while the average positioning stability of the robot's grip is 99.6 %. Time consumption for image processing is 0.053 s while time consumption for robot grip movement is 9 s. Therefore, the stereo vision system can be used to control robot's grip movement with a good accuracy. Keywords: Red chili fruit, Robot arm, Stereo vision, Three-dimensional position.

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

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

End-to-End lightweight Transformer-Based neural network for grasp detection towards fruit robotic handling

Robotic picking and placing are common operations for fruits and vegetables in grading, sorting or packaging systems. However, due to the diverse shapes and irregular surfaces of fruits and vegetables, improper handling during the picking process can result in detachment or damage. To ensure the correct grasping positions, it is necessary to design targeted neural network algorithms for achieving intelligent sorting. Therefore, this study focuses on 20 common fruit and vegetable agricultural products to develop a deep learning-based grasping detection algorithm model. By combining local features from convolutional neural networks with global features from Transformers, a lightweight end-to-end fruit and vegetable grasping detection network, MDETR, is constructed. Experimental results demonstrate that the MDETR algorithm not only achieves high accuracy in fruit and vegetable grasping detection but also improves the speed of pose detection. The average time required for detecting a single image is approximately 29.6 ms, meeting real-time requirements. The algorithm achieves a pose detection accuracy rate of 96 %, enabling precise detection and positioning of fruit and vegetable poses and achieving fast and accurate picking and placing. Additionally, a Pybullet simulation platform is developed for conducting grasping experiments, where the MDETR model achieves a grasping success rate of 88.9 %. This validates the robustness and generalization capabilities of the proposed detection algorithm model, designed specifically for fruit and vegetable grasping tasks.

Light Intensities Affect Canopy Architecture and Fruit Characteristics of Cayenne Pepper (Capsicum frutescens L.)

Mechanical harvesting in cayenne pepper is developing, however, factors affecting canopy architecture and fruit characteristics are still lack. Study aimed to evaluate the effect of shade intensities on canopy architecture and fruit position in cayenne pepper to support developing smart harvesting tools. The experiment was conducted in Babakan Sawah Baru Experimental Farm, IPB from September 2021 to March 2022. The experiment used nested design with shade levels (no shade, 25%, 30%, 50%, 60%, 90%, and 100%) as the main plot and time of shading application (4, 6, 8, and 10 weeks after planting) as sub-plot. The canopy architecture and fruit position were affected by the shade level and its time application. Plant height increased and the canopy widened with increasing shade levels up to 50%. Thus, the shading level should be considered in the development of smart harvesting methodology. Keywords: cabai rawit, climate change, labor, low light intensity, plant architecture

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network.

Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.

Potential of peppers of the genus Capsicum and their use as an ornamental plant

Capsicum is a genus of the Solanaceae family, where the different types of domesticated peppers are found. They are bushy plants, originating from the Americas, producing fruits that are generally used in cooking. The Garden of the School of Agronomy, at the Federal University of Goiás, has a collection of peppers with 167 accessions, where part of these accessions (57) was evaluated to verify their suitability as an ornamental plant. This work was carried out in the experimental area of the Garden of the School of Agronomy at the Federal University of Goiás, in Goiânia – GO Brazil, in the Horticulture sector. The experiment was installed in August 2022 with sowing carried out in resistant plastic trays, with 50 cells, kept in a greenhouse and transplanting carried out in the open area. 5 (five) seedlings were used per accession, where for every 10 (ten accessions of the collection, 5 (five) seedlings of round potted commercial red pepper were transplanted, also added to the collection, with the code (UFGMH070) and the experiment outlined in enlarged blocks. The studied accessions were characterized agronomically, using fifteen descriptors recommended by IPGRI (1995). Among these descriptors, eight are quantitative in nature (plant height (PH), crown diameter (CD), fruit length (FRL), fruit width (FRW), pericarp thickness (PT), number of locules (NL), pericarp weight (PW) and seed weight (SEW) and seven have a qualitative nature (flower color (FLC), position fruit size (FRP), fruit color (FRC), fruit shape (FRS), fruit surface (FRS), seed color (SEC), seed surface (SES). For data analysis, a matrix was estimated of dissimilarity, through the joint analysis of quantitative and qualitative variables, with this matrix, a dendrogram was obtained. The grouping of accesses was obtained using the UPGMA (Unweighted Pair-Group Method Using an Arithmetic Average) method. The data were analyzed by the computational software R. To establish the ornamental value of the plants, the criteria of the Veiling cooperative in Holambra were used, where the variables plant height and position of flowers and fruits, among the descriptors evaluated, are the most important. According to the cluster analysis, there was a division into seven groups and it was possible to observe that Group III had the highest number of accesses, 63.16%, followed by Group II with 15.79%, Group IV with 12.28%. %, Group V with 3.51% and those who were isolated, in groups with only one access Group I 1.75%, Group VI 1.75% and Group VII 1.75%, of the total accesses. Within the dendrogram, the group with the largest number of accessions is Group 3, where in this group are the plants that presented ideal plant height, white flowers and fruits with an upright arrangement, ideal characteristics for their use as an ornamental plant. Agronomic characterization, based on quantitative and qualitative descriptors, enabled an interpretation of genetic dissimilarity, which allows better targeting, for the producer, research, as well as for extension.

Efficient and lightweight grape and picking point synchronous detection model based on key point detection

Precise positioning of fruit and picking point is crucial for harvesting table grapes using automated picking robots in an unstructured agricultural environment. Most studies employ multi-step methods for locating picking points based on fruit detection, leading to slow detection speed, cumbersome models, and algorithmic fragmentation. This study proposes an improved YOLOv8-GP (YOLOv8-Grape and picking point) model based on YOLOv8n-Pose to solve the problem of simultaneous detection of grape clusters and picking points. YOLOv8-GP is an end-to-end network that integrates object detection and key point detection. Specifically, the Bottleneck in C2f is replaced with FasterNet Block that incorporates EMA (Efficient Multi-Scale Attention), resulting in C2f-Faster-EMA. BiFPN is applied to substitute the original PAN as Neck network. The FasterNet Block, designed based on partial convolution (PConv), reduces redundant computation and memory access, thereby extracting spatial features more efficiently. The EMA attention mechanism achieves performance gains with lower computational overhead. Furthermore, BiFPN is employed to enhance the effect of feature fusion. Experimental results demonstrate that YOLOv8-GP achieves AP of 89.7 % for grape cluster detection and a Euclidean distance error of less than 30 pixels for picking point detection. Additionally, the number of Params is reduced by 47.73 %, and the model complexity GFlops is 6.1G. In summary, YOLOv8-GP offers excellent detection performance, while the reduced number of parameters and model complexity contribute to lower deployment costs and easier implementation on mobile robots.

Real-Time Recognition and Localization of Apples for Robotic Picking Based on Structural Light and Deep Learning

The apple is a delicious fruit with high nutritional value that is widely grown around the world. Apples are traditionally picked by hand, which is very inefficient. The development of advanced fruit-picking robots has great potential to replace manual labor. A major prerequisite for a robot to successfully pick fruits the accurate identification and positioning of the target fruit. The active laser vision systems based on structured algorithms can achieve higher recognition rates by quickly capturing the three-dimensional information of objects. This study proposes to combine the laser active vision system with the YOLOv5 neural network model to recognize and locate apples on trees. The method obtained accurate two-dimensional pixel coordinates, which, when combined with the active laser vision system, can be converted into three-dimensional world coordinates for apple recognition and positioning. On this basis, we built a picking robot platform equipped with this visual recognition system, and carried out a robot picking experiment. The experimental findings showcase the efficacy of the neural network recognition algorithm proposed in this study, which achieves a precision rate of 94%, an average precision mAP% of 92.86%, and a spatial localization accuracy of approximately 4 mm for the visual system. The implementation of this control method in simulated harvesting operations shows the promise of more precise and successful fruit positioning. In summary, the integration of the YOLOv5 neural network model with an active laser vision system presents a novel and effective approach for the accurate identification and positioning of apples. The achieved precision and spatial accuracy indicate the potential for enhanced fruit-harvesting operations, marking a significant step towards the automation of fruit-picking processes.

Changes in Yield-Related Traits, Phytochemical Composition, and Antioxidant Activity of Pepper (Capsicum annuum) Depending on Its Variety, Fruit Position, and Ripening Stage

The relationship between fruit position, ripening stage, and variety has not been well studied in pepper plants. To understand the interaction of these factors, a diversity of phytochemical traits as well as antioxidant activity were investigated with agronomic traits in eleven hot pepper varieties collected from the upper and lower parts of the plant and harvested at three maturity stages (green, orange, and red). Capsaicin content (CAP) showed a relatively high genetic effect; on the contrary, total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity were more affected by the ripening stage and fruit position. The CAP values ranged from 0.29 (‘FKbM’) to 0.77 (‘Bka’) mg CAP equivalents g−1 DW. The ripening stage was the predominant factor for TPC, TFC, DPPH, and FRAP. There was no significant interaction between A × FP, A × RS, and FP × RS for all agro-morphological fruit traits. Variety, fruit position, and ripening stage effects are more significant than all interactions calculated. Lower fruit positions in all samples showed a maximum fruit size, whereas phytochemical traits and yield per plant were relevant in the upper parts, and Phytochemical traits and yield per plant were significantly correlated. From PCA and cluster analysis, all varieties showed the highest biochemical and antioxidant levels with moderate fruit size, except the ‘Bel’ variety that showed the smallest fruit traits with high yields, and the ‘FKbM’ and ‘FKbK’ varieties that showed the highest fruit size but low yields. This study supplies information to identify interesting cultivars with considerable levels of bioactive and phytochemical metabolites, which is useful for breeding programs of novel varieties.

Optical properties of melon tissues and Monte Carlo simulation of light propagation through melon fruit

Visible/near-infrared (Vis/NIR) spectroscopy has been authenticated for quality evaluation of melon fruit. However, since melon is a multi-layered fruit with large size, the optical properties (OPs) of different tissue layers can influence the performance of the quality evaluation. Therefore, the OPs of different tissue layers (exocarp, green mesocarp and pulp) among different fruit positions (stem, equator and calyx) of 36 ‘Huanghemi’ melons were measured using single integrating sphere measurements and the light propagation through melon fruit was investigated by the Monte Carlo (MC) simulation based on the OPs. Moreover, the relationship of the OPs with microstructure and internal quality properties (soluble solids content (SSC) and moisture content) was explored. In the absorption coefficient (μa) spectra, five obvious absorption peaks for carotenoids (481 nm), chlorophyll (675 nm) and water (984, 1195 and 1423 nm) were observed. The exocarp had the highest μa values at 481 nm owing to its orange-yellow peel and the lowest μa values at 1423 nm, while the μa values of green mesocarp and pulp had no significant differences at all absorption peaks. The differences in reduced scattering coefficient (μs') spectra were obvious with a decreasing trend from the outermost (exocarp) to the innermost (pulp) layer and the stem to the calyx position in pulp layer, which mainly related to the different tissue structure. The MC simulation results indicated that the energy along the wavelength was mainly absorbed by the exocarp and green mesocarp, with a ‘diagnostic window’ appearing in 700–850 nm. As the source-detector distance increased, the mean optical pathlength and attenuation of detected photons in each tissue layer increased, while the contribution fraction to the detected diffuse reflectance signals by the exocarp, green mesocarp and pulp respectively decreased, increased and increased. To acquire more information about the pulp and ensure a good signal-to-noise ratio of the spectral signal, a source-detector distance of 7 mm with a suitable light source power would be a good choice. Furthermore, the best prediction models with good performance for SSC and moisture content were built based on the μa spectra, with the correlation coefficient of prediction (Rp) of 0.78–0.80, 0.76–0.88 and the root mean square error of prediction (RMSEP) of 0.83–0.92%, 0.70–0.93%. This study was the first attempt to measure the OPs of melon tissues and investigate the light propagation through melon fruit, which would provide a reference to develop nondestructive testing techniques or instruments for melon fruit based on Vis/NIR spectroscopy.

Enhancing cotton’s resilience to waterlogging and salinity stress with exogenous antioxidants

Cotton is classified as moderately salt tolerant crop with salinity threshold level of 7.7 dS m-1. Salinity is a serious threat for cotton growth, yield and fiber quality. The sensitivity to salt and WL stress depends upon growth stage and type of salt. Understanding of cotton response to salinity and WL stress, its resistance mechanism and looking into management techniques may assist in formulating strategies to improve cotton performance under stress condition. The studies have showed that germination, emergence and seedling stages are more sensitive to salinity stress as compared to later stages. Salt & WL stress results in delayed flowering, less fruiting positions, fruit shedding and reduced boll weight which ultimately affect seed cotton yield. It is suggested that future research may be carried out with the combination of conventional and advance molecular technology to develop salt tolerant cultivars.

Robotic Tree-fruit harvesting with arrays of Cartesian Arms: A study of fruit pick cycle times

In some cases, it has been shown that fruits on trees with SNAP (Simple, Narrow, Accessible, and Productive) architectures are reachable by robot arms using three linear degrees of freedom; hence, high fruit-picking efficiencies can be achieved with simpler arms. This paper uses digitized fruit position data to compute the fruit pick cycle times (PCT) of robotic fruit harvesters with multiple arms arranged in grid configurations, i.e., operating in disjoint rectangular work cells independently of each other. The effects of the robot joints’ maximum linear acceleration and maximum linear velocity on PCTs were studied. As Vmax increased, the PCT followed a negative exponent power law (diminishing return) for any given Amax. Similarly, for a constant Vmax, the improvement of PCT as Amax increased slowed down at higher acceleration values.Also, the PCTs were computed when each arm work cell was designed based on equal fruit load or equal size criteria, using four workspace partitioning schemes (height split, length split, height split matrix, and length split matrix). Equal fruit-load configurations resulted in load balancing and exhibited lower PCTs; the height-split configuration was the best among the four different partitioning schemes, possibly because it compensated better for the fruit distribution's non-uniformity along the trees' height.Finally, the PCTs were computed while harvesting one side of an orchard row or both sides concurrently. Harvesting sides separately resulted in lower PCTs (greater speed) due to non-uniform fruit distributions on the different sides of trees. The insights gained in this paper can inform the design of harvesting robots utilizing arrays of 3-dof linear arms.

Study of the Group Vibrational Detachment Characteristics of Litchi (Litchi chinensis Sonn) Clusters

A high-speed camera was applied to observe the shaking detachment tests with ‘Guiwei’ litchi clusters using 7 Hz, 9.5 Hz, and 12 Hz and the amplitudes of 40 mm, 60 mm, and 80 mm. The fruit’s detachment time and the number of fruit–fruit impacts were affected by the vibrational parameters and the fruit position (inside or outside of the cluster). A high and stable detachment speed and a low number of impacts could be achieved with the parameter combination of 9.5 Hz/80 mm. The cluster index, considering both the shaking position and the total fruit mass, was found to have a stronger correlation with the detachment time than the average fruit-to-shaker distance (distance from the shaker to the fruit). Thus, the detachment time can be estimated based on the cluster indexes. The fruit-to-shaker distance also affected the fruit-to-shaker impact. A fruit-to-shaker distance of 50~60 mm was recommended to avoid almost all the impacts, depending on the shaking parameters. The horizontal throw distance of the fruit was calculated based on the detachment velocity and angle, which can be used as the design basis for the catching devices. With a high enough drop height, a disc-shaped catching surface with a radius of 475 mm, 489 mm, 591 mm, 741 mm, 844 mm, and 832 mm, respectively, could collect 90% of all the fruit at six different vibrational parameter combinations. A closer catching surface to the fruit could effectively reduce the horizontal throw distance.