Fruit Segments Research Articles

Multi-scale and multi-receptive field-based feature fusion for robust segmentation of plant disease and fruit using agricultural images

The accurate and fast assessment of plant diseases and fruits is important for sustainable and productive agriculture. However, typically manual methods are adopted for the assessment of plant diseases and fruit, which is a time-consuming, resource-intensive, and error-prone approach. A few artificial intelligence (AI)-based methods have been introduced to automate this process, but they show limitations in attaining high performance in challenging imaging conditions such as occlusions, poor lighting, noise, indistinctive boundaries, and excessive morphological variations. Moreover, current approaches also lack in providing computationally efficient solutions. To overcome these issues, two novel architectures are developed to segment plant diseases and fruits with higher segmentation performance and less computational requirements. The efficient feature fusion segmentation network (EFFS-Net) is the base network, and a multi-scale dilated feature fusion segmentation network (MDFS-Net) is the final network of this study. EFFS-Net uses an identity skip path-based feature fusion mechanism with an efficient grouped convolutional depth (EGCD) to provide satisfactory segmentation performance with high computational efficiency. MDFS-Net uses a multi-scale feature fusion mechanism and fuses low-level information in the EGCD and other sections of the network to learn detailed input features for delivering promising performance even in challenging imaging conditions. MDFS-Net also applies multiple receptive fields to the low-level information in the effective receptive field processing (ERFP) block for fusion near the pixel classification stage for further performance enhancement.Both networks are evaluated using a Brazilian Arabica coffee leaf (BRACOL) image dataset, an Australian Center for Field Robotics orchard fruit (apple) dataset, and a necrotized cassava root cross-section image dataset. The proposed method provides a promising segmentation performance, achieving dice similarity coefficients of 88.81 %, 95.01 %, and 86.04 % with performance improvement of 3.5 %, 2.6 %, and 1.07 % on the three datasets, respectively, with approximately ten times less number of required trainable parameters compared with the state-of-the-art methods.

FBSM: Foveabox-based boundary-aware segmentation method for green apples in natural orchards

Image segmentation of target fruits is an essential part of machine vision systems, aiming to facilitate more accurate early fruit measurement and machine harvesting in natural orchard environments. Given that instance-level pooling and down-sampling operations in conventional segmentation models often lead to the loss of detailed information, resulting in coarse partitioning masks, we endeavor to restore the boundary information of the masks. To achieve high-quality fruit segmentation with clear boundaries and refined masks, a Foveabox-based boundary-aware segmentation model (FBSM) is constructed by adding a multi-stage mask prediction head incorporating fine-grained features to the anchor-free detection model FoveaBox to realize accurate segmentation of green apple fruits. At each stage, a bi-layer fusion structure (BFS) is employed to fully fuse the fine-grained features in a double-fusion manner to guide the subsequent instance mask prediction. Finally, the boundary recovery module (BRM) is leveraged to recover the lost boundary detail information and obtain more accurate boundaries for the continuous optimization process of the fruit instance mask. Experimental results demonstrate that the proposed FBSM model achieves a mean average precision (mAP) of 60.7 % for green apple instance segmentation in unstructured natural orchard environments, surpassing traditional instance segmentation models. Moreover, it enhances base model detection accuracy by 1.3 % while striking a better balance between detection and segmentation performance.

RTMFusion: An enhanced dual-stream architecture algorithm fusing RGB and depth features for instance segmentation of tomato organs

Accurate and rapid acquisition of semantically annotated point clouds from tomato cultivation regions is crucial for autonomous tomato harvesting robots. This entails the robot’s ability to perceive tomato point clouds from various locations and identify the stems and pedicels connecting the fruits. However, amidst background interference from similar stem and pedicel features, the heterogeneous growth postures of tomato clusters and the slender and short nature of pedicels pose challenges in semantic point cloud segmentation and matching of different tomato fruits, pedicels, and stems. Leveraging the salient feature representation of depth images in fine contours and distance dimensions, this paper introduces RTMFusion, a lightweight dual-stream backbone instance segmentation model that integrates color images and depth images. A Depth Enhanced Feature Fusion module is designed for multi-modal feature fusion. Fruit-bearing organ matching is facilitated by a method based on distance constraints and conditional search. Key characteristics are extracted from semantic point clouds, and an optimal condition search is conducted on candidates meeting constraint conditions. On the test dataset, our model improved the mask mAP by 4.6 % compared to the baseline, achieving 72.1 % with a speed of 17.1 ms. Our matching method achieved 99.2 % pedicel matching accuracy and 97.3 % stem matching accuracy on 150 greenhouse samples. These results demonstrate the effectiveness of accurately identifying and matching tomato organs in real-world scenarios, addressing the challenge from complex greenhouse environments. This integrated visual system enhances the success rate of visual identification and the precision of cutting position localization for tomato harvesting robots.

Physiological and Transcriptomic Analysis of Citrus Fruit Segment Drying under Facility-Forcing Cultivation

Facility-forcing cultivation could effectively improve the quality of ‘Beni Madonna’ citrus (Citrus nankao × C. amakusa) and advance the ripening period. However, segment drying (KS) before harvest caused fruit quality deterioration and commodity value loss. In this research, we investigated the physiological and molecular characteristics involved in citrus segment drying under facility-forcing cultivation. The juice yield, sugar, acid, vitamin C, and lignin contents in KS fruits were significantly decreased, and the contents of pectin and cellulose were significantly increased. The relative contents of abscisic acid and abscisic acid glucosyl ester in KS fruits were significantly decreased. A total of 1215 differentially expressed genes (DEGs) were screened by transcriptome sequencing. DEGs were significantly enriched in water metabolism, sugar metabolism, transportation, cell wall and phenylpropanoid biosynthesis, and plant hormone signal transduction. The decrease in water absorption and sugar synthesis, the increase of pectin and cellulose synthesis, and the decrease in ABA accumulation may be the main reasons that cause citrus fruit segments to dry under facility-forcing cultivation.

High-Precision Peach Fruit Segmentation under Adverse Conditions Using Swin Transformer

High-Precision Peach Fruit Segmentation under Adverse Conditions Using Swin Transformer

Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.

Citrus fruits are extensively cultivated fruits with high nutritional value. The identification of distinct ripeness stages in citrus fruits plays a crucial role in guiding the planning of harvesting paths for citrus-picking robots and facilitating yield estimations in orchards. However, challenges arise in the identification of citrus fruit ripeness due to the similarity in color between green unripe citrus fruits and tree leaves, leading to an omission in identification. Additionally, the resemblance between partially ripe, orange-green interspersed fruits and fully ripe fruits poses a risk of misidentification, further complicating the identification of citrus fruit ripeness. This study proposed the YOLO-CIT (You Only Look Once-Citrus) model and integrated an innovative R-LBP (Roughness-Local Binary Pattern) method to accurately identify citrus fruits at distinct ripeness stages. The R-LBP algorithm, an extension of the LBP algorithm, enhances the texture features of citrus fruits at distinct ripeness stages by calculating the coefficient of variation in grayscale values of pixels within a certain range in different directions around the target pixel. The C3 model embedded by the CBAM (Convolutional Block Attention Module) replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-CIT model. Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. The experiment showed that the R-LBP algorithm is capable of amplifying the texture features among citrus fruits at distinct ripeness stages. The YOLO-CIT model combined with the R-LBP algorithm has a Precision of 88.13%, a Recall of 93.16%, an F1 score of 90.89, a mAP@0.5 of 85.88%, and 6.1ms of average detection speed for citrus fruit ripeness identification in complex environments. The model demonstrates the capability to accurately and swiftly identify citrus fruits at distinct ripeness stages in real-world environments, effectively guiding the determination of picking targets and path planning for harvesting robots.

HPPEM: A High-Precision Blueberry Cluster Phenotype Extraction Model Based on Hybrid Task Cascade

Blueberry fruit phenotypes are crucial agronomic trait indicators in blueberry breeding, and the number of fruits within the cluster, maturity, and compactness are important for evaluating blueberry harvesting methods and yield. However, the existing instance segmentation model cannot extract all these features. And due to the complex field environment and aggregated growth of blueberry fruits, the model is difficult to meet the demand for accurate segmentation and automatic phenotype extraction in the field environment. To solve the above problems, a high-precision phenotype extraction model based on hybrid task cascade (HTC) is proposed in this paper. ConvNeXt is used as the backbone network, and three Mask RCNN networks are cascaded to construct the model, rich feature learning through multi-scale training, and customized algorithms for phenotype extraction combined with contour detection techniques. Accurate segmentation of blueberry fruits and automatic extraction of fruit number, ripeness, and compactness under severe occlusion were successfully realized. Following experimental validation, the average precision for both bounding boxes (bbox) and masks stood at 0.974 and 0.975, respectively, with an intersection over union (IOU) threshold of 0.5. The linear regression of the extracted value of the fruit number against the true value showed that the coefficient of determination (R2) was 0.902, and the root mean squared error (RMSE) was 1.556. This confirms the effectiveness of the proposed model. It provides a new option for more efficient and accurate phenotypic extraction of blueberry clusters.

On-tree fruit image segmentation comparing Mask R-CNN and Vision Transformer models. Application in a novel algorithm for pixel-based fruit size estimation

In situ automatic fruit monitoring is of great interest for more accurate and cost-efficient decision making in agriculture. For this purpose, the development of computer vision-based tools is essential. Deep Learning techniques have shown good performance in fruit detection and segmentation. Recently, new models based on Transformer architecture have emerged with promising potential and zero-shot inference capability. In this paper, a Deep Learning model, Mask R-CNN, was trained for on-tree pomegranate fruit segmentation and compared with foundational models based on Vision Transformer, Grounding DINO and Segment Anything Model. Results with Mask R-CNN proved a better performance, according to F1 score and AP metrics, and a lower computational cost, according to prediction time. One of the most interesting derived applications from fruit segmentation is fruit size estimation. However, segmented fruit masks are frequently incomplete due to occlusions. Therefore, image fruit size estimation is not a straightforward process. In this work, we also propose a novel algorithm to estimate and monitor the fruit size in pixel units from the automated masks. A median relative error of 1.39% was obtained, demonstrating the potential and feasibility of future fully-automatic fruit size estimators.

Classification of Chili Fruit Diseases Using Deep Convolutional Neural Network Transfer Learning

Chili peppers are among the highest-value agricultural commodities, often experiencing significant price fluctuations due to supply constraints. The rainy season frequently leads to crop failures caused by diseases affecting chili plants. Existing methods often struggle to accurately differentiate between similar symptoms on leaves and fruits, leading to misdiagnosis and ineffective disease management strategies. Early detection of these diseases, which manifest as symptoms on the leaves and fruits, is crucial for effective pest management. Common diseases include anthracnose, characterized by dry brown spots on the fruit, and fruit rot, where the interior of the fruit decays while the skin remains intact. Identifying these diseases promptly is essential for applying appropriate treatments to ensure optimal yields.In this study, a comprehensive approach is taken to classify diseases in chili pepper plants (Capsicum annuum L.) by incorporating both leaf and fruit segmentation. The research employs Deep Convolutional Neural Networks with Transfer Learning (DCNN) to enhance detection capabilities. The findings reveal that for leaf disease classification, fewer neurons in additional layers yield better accuracy and reduced loss, while for fruit disease classification, a more complex model with additional neurons is necessary. This underscores the need for balancing model complexity to achieve optimal performance and prevent overfitting, particularly in distinguishing between leaf and fruit diseases.

NVW-YOLOv8s: An improved YOLOv8s network for real-time detection and segmentation of tomato fruits at different ripeness stages

Tomato yield estimation relies significantly on the accurate detection of fruit quantity and size. And object detection and semantic segmentation of fruits emerge as efficacious methodologies for the realization of fruit counting and size detection. To address challenges associated with the detection and segmentation of tomato fruits in complex environments such as sample imbalance of different classes, small targets, and susceptibility to occlusion at varying stages of ripeness, this study proposed a foreground-foreground class balance method and an improved YOLOv8s network, NVW-YOLOv8s, for detecting and segmenting tomato fruits simultaneously. The foreground-foreground class balance method initially performed pixel-wise extraction on fruit samples with fewer instances. Subsequently, it synthesized them with original images containing a limited number of samples from the class, thereby augmenting the overall quantity of this specific category of fruit samples. In the NVW-YOLOv8s network, a C2f-N module, founded on the normalization-based attention module (NAM), was specifically crafted for residual feature learning. This design serves to augment the network's proficiency in extracting and integrating feature information pertaining to tomato fruits within intricate environments. Additionally, a variable focal loss (VFL) was introduced as the classification loss function to address the issue of positive and negative sample imbalance, and a regression loss function based on Wise-IoU (WIoU) was incorporated to tackle challenges related to fruit small targets and susceptibility to occlusion. The YOLOv8s models trained with the augmented balanced dataset revealed that the model's detection and segmentation performance, as indicated by mAP@0.5, improved by 4.8 % and 5.4 %, respectively, compared with the model trained with the augmented original training dataset. The test results on the augmented balanced dataset indicated that the proposed NVW-YOLOv8s network achieved a detection mAP@0.5 of 91.4 %, an F1-Score of 85.4 %, a segmentation mAP@0.5 of 90.7 %, and an F1-Score of 84.8 %. These results surpass the baseline YOLOv8s network by 4.3 % and 5.5 % for detection, 4.1 % and 5.0 % for segmentation, respectively. Additionally, the processing for concurrent detection and segmentation was measured at a speed of 60.2 FPS. Therefore, the proposed method has successfully met the precision and real-time requirements for intelligent yield estimation in horticultural crops.

Occlusion-aware fruit segmentation in complex natural environments under shape prior

Widespread occlusion in complex natural environments severely restricts the accurate segmentation and localization of approximately spherical fruits by existing automatic picking devices, resulting in the confinement of efficient automatic picking to a controlled laboratory environment. While occlusion can be mitigated to some extent by optimizing annotations and refining the algorithm, the absence of partial fruit information due to occlusion may still result in misplacement or incorrect localization of the picking point. In this study, by introducing approximately spherical fruit shape priors, the widespread partial occlusion, slice occlusion, and self-occlusion found in complex natural environments can be effectively addressed. First, with the help of a boundary interest point–based fruit region contour segmentation method, the proposed method can achieve more accurate fruit segmentation in a complex environment. Self-occlusion can be mitigated with better separation and accurate localization for under-segmented overlapping fruit regions, which is achieved in this study through a local minima point–based fruit region contour segmentation method. This research demonstrates that occlusion-aware fruit segmentation can be achieved simply by introducing the approximately spherical fruit shape priors that are easily available in real production. Additional experiments are also performed to illustrate that the shape priors can be flexibly adjusted to ensure the generalizability of the proposed method to different species of fruits. The proposed method, which utilizes only plant phenotypic information and refrains from reliance on additional training data or equipment, can effectively solve the ubiquitous occlusion problem in complex natural environments and is important for future large-scale automatic fruit picking in smart agriculture.

CucumberAI: Cucumber Fruit Morphology Identification System Based on Artificial Intelligence.

Cucumber is an important vegetable crop that has high nutritional and economic value and is thus favored by consumers worldwide. Exploring an accurate and fast technique for measuring the morphological traits of cucumber fruit could be helpful for improving its breeding efficiency and further refining the development models for pepo fruits. At present, several sets of measurement schemes and standards have been proposed and applied for the characterization of cucumber fruits; however, these manual methods are time-consuming and inefficient. Therefore, in this paper, we propose a cucumber fruit morphological trait identification framework and software called CucumberAI, which combines image processing techniques with deep learning models to efficiently identify up to 51 cucumber features, including 32 newly defined parameters. The proposed tool introduces an algorithm for performing cucumber contour extraction and fruit segmentation based on image processing techniques. The identification framework comprises 6 deep learning models that combine fruit feature recognition rules with MobileNetV2 to construct a decision tree for fruit shape recognition. Additionally, the framework employs U-Net segmentation models for fruit stripe and endocarp segmentation, a MobileNetV2 model for carpel classification, a ResNet50 model for stripe classification and a YOLOv5 model for tumor identification. The relationships between the image-based manual and algorithmic traits are highly correlated, and validation tests were conducted to perform correlation analyses of fruit surface smoothness and roughness, and a fruit appearance cluster analysis was also performed. In brief, CucumberAI offers an efficient approach for extracting and analyzing cucumber phenotypes and provides valuable information for future cucumber genetic improvements.

CottonSense: A high-throughput field phenotyping system for cotton fruit segmentation and enumeration on edge devices

High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton (Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R2 value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R2 value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense.

Mask R-CNN aided fruit surface temperature monitoring algorithm with edge compute enabled internet of things system for automated apple heat stress management

Our prior study focused on development of internet of things (IoT) and edge-compute enabled crop physiology sensing system (CPSS) for apple sunburn monitoring. Edge compute algorithm on CPSS estimated sunburn susceptibility as fruit surface temperature (FST) through pixel-by-pixel multiplication of captured thermal infrared images with segmented fruits binary mask. The segmentation was performed using color-based K means clustering approach. This limited CPSS applicability to monitor sunburn of red colored cultivars only and when fruits develop color, typically late growing season. This is a key research gap as recent weather patterns have shown that sunburn can occur during early growing season when fruits are green to yellow. Therefore, aim of this study was to develop and field evaluate cultivar and color independent mask region-convolution neural network (R-CNN) aided fruit segmentation model and edge compute compatible FST estimation algorithm. Season long field data were collected in 2021 using eight CPSS nodes (three in cv. WA38 [Cosmic crisp] and five in cv. Honeycrisp). Collected data were used to develop and validate mask R-CNN based fruit segmentation model. Developed mask R-CNN based model was able to segment fruits of two apple cultivars and of varying colors with 91.4 % average precision. In orchard evaluations (2022 season), the resulting algorithm ported on CPSS was able to accurately segment (dice similarity coefficient = 0.89) and estimate apple FST with < 0.5 °C error compared to ground truth data. With compute time of about 37 s, data processing time was reduced by 22 % over previous algorithm. High ambient temperature (>35 °C) on a warmer day resulted in multiple throttling errors caused by excessive CPU temperature; however, the CPSS performance was uncompromised in FST estimation. Ambient air temperature did not affect RAM utilization and CPU clock frequency. Overall, developed FST algorithm can potentially be used as input to actuate water-based cooling system.

ASE-UNet: An Orange Fruit Segmentation Model in an Agricultural Environment Based on Deep Learning

ASE-UNet: An Orange Fruit Segmentation Model in an Agricultural Environment Based on Deep Learning

Segmentation of Leaves and Fruits of Tomato Plants by Color Dominance

The production of food generated by agriculture has been essential for civilizations throughout time. Tillage of fields has been supported by great technological advances in several areas of knowledge, which have increased the amount of food produced at lower costs. The use of technology applied to modern agriculture has generated a research area called precision agriculture, which has providing crops with resources in an exact amount at a precise moment as one of its most relevant objectives The data analysis process in precision agriculture systems begins with the filtering of the information available, which can come from sources such as images, videos, and spreadsheets. When the information source is digital images, the process is known as segmentation, which consists of assigning a category or label to each pixel of the analyzed image. In recent years, different algorithms of segmentation have been developed that make use of different pixel characteristics, such as color, texture, neighborhood, and superpixels. In this paper, a method to segment images of leaves and fruits of tomato plants is presented, which is carried out in two stages. The first stage is based on the dominance of one of the color channels over the other two, using the RGB color model. In the case of the segmentation of the leaves, the green channel dominance is used, whereas the dominance of red channel is used for the fruits. In the second stage, the false positives generated during the previous stage are eliminated by using thresholds calculated for each pixel that meets the condition of the first stage. The results are measured by applying performance metrics: Accuracy, Precision, Recall, F1-Score, and Intersection over Union. The results for segmentation of the fruit and leaves of the tomato plants with the highest metrics is Accuracy with 98.34% for fruits and Recall with 95.08% for leaves.

Molecular physiology for the increase of soluble sugar accumulation in citrus fruits under drought stress

To investigate the mechanism for drought promoting soluble sugar accumulation will be conducive to the enhancement of citrus fruit quality as well as stress tolerance. Fruit sucrose mainly derives from source leaves. Its accumulation in citrus fruit cell vacuole involves in two processes of unloading in the fruit segment membrane (SM) and translocating to the vacuole of fruit juice sacs (JS). Here, transcript levels of 47 sugar metabolism- and transport-related genes were compared in fruit SM or JS between drought and control treatments. Results indicated that transcript levels of cell wall invertase genes (CwINV2/6) and sucrose synthase genes (SUS2/6) in the SM were significantly increased by the drought. Moreover, transcript levels of SWEET genes (CsSWEET1/2/4/5/9) and monosaccharide transporter gene (CsPMT3) were significantly increased in SM under drought treatment. On the other hand, SUS1/3 and vacuolar invertase (VINV) transcript levels were significantly increased in JS by drought; CsPMT4, sucrose transporter gene 2 (CsSUT2), tonoplast monosaccharide transporter gene 2 (CsTMT2), sugar transport protein gene 1 (CsSTP1), two citrus type I V-PPase genes (CsVPP1, and CsVPP2) were also significantly increased in drought treated JS. Collectively, the imposition of drought stress resulted in more soluble sugar accumulation through enhancing sucrose download by enhancing sink strength- and transport ability-related genes, such as CwINV2/6, SUS2/6, CsSWEET1/2/4/5/9, and CsPMT3, in fruit SM, and soluble sugar storage ability by increasing transcript levels of genes, such as CsPMT4, VINV, CsSUT2, CsTMT2, CsSTP1, CsVPP1, and CsVPP2, in fruit JS.

Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (Ananas comosus).

With increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (Ananas comosus) is on the rise, favored by the sensory quality of the product and mechanization of the cutting process. In this paper, a multispectral imaging-based approach is introduced to provide distribution maps of moisture content, soluble solids content, and carotenoids content in fresh-cut pineapple. A dataset containing hyperspectral images (380-1690 nm) and reference measurements in 10 regions of interest of 60 fruit (n = 600) was prepared. Ranking and uncorrelatedness (based on ReliefF algorithm) and subset selection (based on CfsSubset algorithm) approaches were applied to find the most informative wavelengths in which bandpass optical filters or light sources are commercially available. The correlation coefficient and error metrics obtained by cross-validated multilayer perceptron neural network models indicated that the superior selected wavelengths (495, 500, 505, 1215, 1240, and 1425 nm) resulted in prediction of moisture content with R = 0.56, MAPE = 1.92%, soluble solids content with R = 0.52, MAPE = 14.72%, and carotenoids content with R = 0.63, MAPE = 43.99%. Prediction of chemical composition in each pixel of the multispectral images using the calibration models yielded spatially distributed quantification of the fruit slice, spatially varying according to the maturation of single fruitlets in the whole pineapple. Calibration models provided reliable responses spatially throughout the surface of fresh-cut pineapple slices with a constant error. According to the approach to use commercially relevant wavelengths, calibration models could be applied in classifying fruit segments in the mechanized preparation of fresh-cut produce.

Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage

The spread of infections and rot are crucial factors in the decrease in tomato production. Accurately segmenting the affected tomatoes in real-time can prevent the spread of illnesses. However, environmental factors and surface features can affect tomato segmentation accuracy. This study suggests an improved YOLOv8s-Seg network to perform real-time and effective segmentation of tomato fruit, surface color, and surface features. The feature fusion capability of the algorithm was improved by replacing the C2f module with the RepBlock module (stacked by RepConv), adding SimConv convolution (using the ReLU function instead of the SiLU function as the activation function) before two upsampling in the feature fusion network, and replacing the remaining conventional convolution with SimConv. The F1 score was 88.7%, which was 1.0%, 2.8%, 0.8%, and 1.1% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm, respectively. Meanwhile, the segment mean average precision (segment mAP@0.5) was 92.2%, which was 2.4%, 3.2%, 1.8%, and 0.7% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm. The algorithm can perform real-time instance segmentation of tomatoes with an inference time of 3.5 ms. This approach provides technical support for tomato health monitoring and intelligent harvesting.

Metabolite Diversity in Pulp Segments, Peel, Leaves, and Bark of a Red-Fleshed ‘Baya Marisa’ Apple Cultivar

This study investigated the composition of sugars, organic acids, individual and total phenolic compounds in the pulp, peel, leaves, and bark of a red-fleshed ‘Baya Marisa’ apple cultivar. As the fruit is known for its red pulp color, the study focused on comparing the profiles of primary and selected secondary metabolites in three sections along the equatorial fruit plane. The analyses were carried out using HPLC–MS system, and compositional differences were compared among fruit segments. Inner fruit segments accumulated higher levels of sorbitol and the sum of sugars and lower levels of citric acid. However, no differences in the sum of organic acids could be determined among segments. The phenolic composition differed among pulp (hydroxycinnamic acids &gt; dihydrochalcones ≈ anthocyanins ≈ flavanols &gt; flavonols), peel (flavanols &gt; anthocyanins &gt; dihydrochalcones &gt; flavonols &gt; hydroxycinnamic acids), leaves (flavonols &gt; dihydrochalcones &gt; hydroxycinnamic acids &gt; anthocyanins), and bark (dihydrochalcones &gt; flavonols &gt; flavanols &gt; anthocyanins &gt; hydroxycinnamic acids). The greatest phenolic diversity was detected in bark (25), followed by leaves (18), peel (17) and finally, pulp (11). Three anthocyanins (cyanidin-3-O-galactoside &gt; cyanidin-3-O-arabinoside &gt; peonidin-3-O-galactoside) were determined in all ‘Baya Marisa’ tissues with their content highest in the peel. The innermost sections of the fruit were characterized by higher levels of dihydrochalcones and lower levels of most flavanols, flavonols and anthocyanins. These were predominant in the apple pulp nearest to the peel, with cyanidin-3-O-galactoside being the prevalent representative. Accumulation of anthocyanins in pulp is a rare trait in apples, and therefore, the distinct distribution and diversity of metabolites in this cultivar highlights its potential for high-nutrient products such as juices, apple chips or purees.