Strawberry Maturity Research Articles

An Efficient and Lightweight YOLOv8s Strawberry Maturity Detection Model

An Efficient and Lightweight YOLOv8s Strawberry Maturity Detection Model

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

A lightweight strawberry detection and localization algorithm plays a crucial role in enabling the harvesting robot to effectively harvest strawberries. The YOLO model has often been used in strawberry fruit detection for its high accuracy, speed, and robustness. However, some challenges exist, such as the requirement for large model sizes, high computation operation, and undesirable detection. Therefore, the lightweight improved YOLOv5s-CGhostnet was proposed to enhance strawberry detection. In this study, YOLOv5s underwent comprehensive model compression with Ghost modules GCBS and GC3, replacing modules CBS and C3 in the backbone and neck. Furthermore, the default GIOU bounding box regressor loss function was replaced by SIOU for improved localization. Similarly, CBAM attention modules were added before SPPF and between the up-sampling and down-sampling feature fusion FPN–PAN network in the neck section. The improved model exhibited higher mAP@0.5 of 91.7% with a significant decrement in model size by 85.09% and a reduction in GFLOPS by 88.5% compared to the baseline model of YOLOv5. The model demonstrated an increment in mean average precision, a decrement in model size, and reduced computation overhead compared to the standard lightweight YOLO models.

Rapid Strawberry Ripeness Detection And 3D Localization of Picking Point Based on Improved YOLO V8-Pose with RGB-Camera

Accurate identification of strawberries at different growth stages as well as determination of optimal picking points by strawberry picking robots is a key issue in the field of agricultural automation. In this paper, a fast detection method of strawberry ripeness and picking point based on improved YOLO V8-Pose (You Only Look Once) and RGB-D depth camera is proposed to address this problem. By comparing the YOLO v5-Pose, YOLO v7-Pose, and YOLO v8-Pose models, it is determined to use the YOLO v8-Pose model as the fundamental model for strawberry ripeness and picking point detection. For the sake of further improving the accuracy of the model detection, this paper makes targeted improvements: all the Concat modules at the Neck part are replaced with BiFPN richer feature fusion, which enhances the global feature extraction capability of the model; the MobileViTv3 framework is employed to restructure the backbone network, thereby augmenting the model's capacity for contextual feature extraction. Subsequently, the output-side CIoU loss function is supplanted with the SIoU loss function, leading to an acceleration in the model's convergence. The enhanced YOLO v8-Pose demonstrates a 97.85% mAP-kp value, reflecting a 5.49% improvement over the initial model configuration.. For the sake of accurately localizing the three-dimensional information of strawberry picking points, the strawberry picking points are further projected into the corresponding depth information to obtain their three-dimensional information. The experimental results show that the mean absolute error and the mean absolute percentage error of strawberry picking point localization in this paper are 0.63 cm and 1.16%, respectively. In this study, we introduce a method capable of concurrently detecting strawberry maturity and identifying the precise harvesting location while accurately localizing the picking point. This investigation holds considerable theoretical and pragmatic relevance in augmenting the intelligence of strawberry harvesting robots and actualizing automation and smart capabilities in agricultural production.

Genome-wide analysis and functional validation reveal the role of late embryogenesis abundant genes in strawberry (Fragaria × ananassa) fruit ripening

BackgroundLate embryogenesis abundant (LEA) proteins play important roles in plant growth and development, as well as stresses responsiveness. Nowadays, it has been found that LEAs also have function in fruit ripening. However, the comprehensive analysis on a genome-wide basis of LEA family remains limited, and the role of LEA in fruit ripening has not been fully explored yet, especially in strawberry, an economic important plant and ideal material for studying fruit ripening.ResultsIn this study, a total of 266 putative LEA proteins were identified and characterized in strawberry genome. Subcellular localization prediction indicated that they were mostly localized in chloroplast, cytoplasm and nucleus. Duplication events detection revealed that whole genome duplication or segmental was the main driver for the expansion of LEA family in strawberry. The phylogenetic analysis suggested that FaLEAs were classified into eight groups, among which, LEA2 was the largest subgroup with 179 members, followed by LEA3, dehydrin (DHN), LEA4 and SMP (seed maturation protein). The LEA1 and DHN groups were speculated to play dominant roles in strawberry fruit development and ripening, according to their larger proportion of members detected as differentially expressed genes during such process. Notably, the expression of FaLEA167 belonging to LEA1 group was altered by strawberry maturation, and inhibited by overexpression of negative regulators of ripening (a cytosolic/plastid glyceraldehyde-3-phosphate dehydrogenase, FaGAPC2 and a cytosolic pyruvate kinase, FaPKc2.2). Subsequently, overexpression of FaLEA167 significantly increased the percentage of fruit at green stage, while reduced the full red fruit proportion. In consistent, the anthocyanins content and the fruit skin color variable reflecting a range from greenness to redness (a* value) were significantly reduced. Whereas, FaLEA167 overexpression apparently up-regulated citric acid, soluble protein and malondialdehyde content, but had no obvious effects on total soluble solids, sugar, flavonoids, phenolics content and antioxidant capacity.ConclusionsThese findings not only provided basic information of FaLEA family for further functional research, but also revealed the involvement of FaLEA167 in negatively regulating strawberry fruit ripening, giving new insights into understanding of FaLEA functions.

Strawberry Maturity Recognition Based on Improved YOLOv5

Strawberry maturity detection plays an essential role in modern strawberry yield estimation and robot-assisted picking and sorting. Due to the small size and complex growth environment of strawberries, there are still problems with existing recognition systems’ accuracy and maturity classifications. This article proposes a strawberry maturity recognition algorithm based on an improved YOLOv5s model named YOLOv5s-BiCE. This algorithm model is a replacement of the upsampling algorithm with a CARAFE module structure. It is an improvement on the previous model in terms of its content-aware processing; it also widens the field of vision and maintains a high level of efficiency, resulting in improved object detection capabilities. This article also introduces a double attention mechanism named Biformed for small-target detection, optimizing computing allocation, and enhancing content perception flexibility. Via multi-scale feature fusion, we utilized double attention mechanisms to reduce the number of redundant computations. Additionally, the Focal_EIOU optimization method was introduced to improve its accuracy and address issues related to uneven sample classification in the loss function. The YOLOv5s-BiCE algorithm was better at recognizing strawberry maturity compared to the original YOLOv5s model. It achieved a 2.8% increase in the mean average precision and a 7.4% increase in accuracy for the strawberry maturity dataset. The improved algorithm outperformed other networks, like YOLOv4-tiny, YOLOv4-lite-e, YOLOv4-lite-s, YOLOv7, and Fast RCNN, with recognition accuracy improvements of 3.3%, 4.7%, 4.2%, 1.5%, and 2.2%, respectively. In addition, we developed a corresponding detection app and combined the algorithm with DeepSort to apply it to patrol robots. It was found that the detection algorithm exhibits a fast real-time detection speed, can support intelligent estimations of strawberry yield, and can assist picking robots.

Investigating the relationship between strawberry maturity and glossiness on the surface during storage through digital image analysis

Investigating the relationship between strawberry maturity and glossiness on the surface during storage through digital image analysis

YOLO기반의 객체인식을 통한 딸기 수확 로봇 시스템에 대한 연구

Recently, interest in intelligent agricultural robots has surged due to the aging of rural workers, leading to active research on agricultural automation. We designed a strawberry harvesting robot that moves along a rail, equipped with a 3-axis linear actuator, an RGB-Depth camera for object detection, and a rotary gripper for branch cutting. Our research focused on developing algorithms for strawberry maturity classification using an AI vision system, calculating cutting points for fruit acquisition, and implementing these algorithms in a robot. A convolutional neural network based on YOLO was employed for object detection, and representation learning was used to determine the picking point with an ROI(Region Of Interest) image derived from object detection. Our strawberry harvesting robot system boasts an average harvesting success rate of 90% for ripe fruits.

Research on Strawberry Maturity Detection Technology Based on Improved YOLOv4

Strawberry maturity detection is a key technology for automated strawberry picking and intelligent information monitoring. This paper studies strawberry maturity detection technology, and proposes an improved YOLOv4 convolutional neural network detection method. In view of great amount of network parameters, this paper uses MobileNetv3 backbone feature extraction network and depthwise separable convolution for lightweight improvement of the YOLOv4 network. In order to increase the model training accuracy, this paper uses the Kmeans++ clustering algorithm to calculate the prior bounding box size, and uses transfer learning and staged training methods to improve the training efficiency of the built network model. The experimental results show that the mean average precision (mAP) of the test dataset in this paper is 96.78%; the precision of mature strawberry detection is 98.72%, with recall rate 91.67% and average precision (AP) 99.56%; the precision of immature strawberry detection is 90.76%, with recall rate 83.92% and AP 94.00%. Single image detection time is 56ms, which can meet the demand for real-time high-precision detection of strawberries.

Quality Analysis Prediction and Discriminating Strawberry Maturity with a Hand-held Vis–NIR Spectrometer

Quality Analysis Prediction and Discriminating Strawberry Maturity with a Hand-held Vis–NIR Spectrometer

Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning

Strawberry is ranked third in the value of production of the crops in Florida, USA. Classifying strawberry maturity and monitoring strawberry growth status in the field is very critical for accurate strawberry yield prediction, efficient strawberry field management, and achieving the highest crop production. The traditional method of distinguishing strawberry maturity is based on either physical appearance or internal chemical substance content. However, the traditional method is time-consuming and costly. In this research, an automatic strawberry maturity classification system was developed for the rapid and accurate classification of different strawberry maturity stages. A state-of-the-art deep learning method, You Only Look Once (YOLOv3), which is good at small object detection, was trained and applied to detect strawberry flowers and strawberry fruit at different maturity stages. Two strawberry image acquisition methods, aerial imaging and near-ground imaging, were compared by using the same deep learning image processing method. As a result, three and seven strawberry maturity stages were classified for unmanned aerial vehicle (UAV) images and near-ground digital camera images, respectively. For UAV images, the highest mean average precision (mAP) of strawberry maturity classification was 0.88 for a test data set at 2 m, and the highest classification average precision (AP) was 0.93 for fully matured fruit. For near-ground digital camera images, the mAP of strawberry maturity classification was 0.89, and the highest classification AP was 0.94 for fully matured fruit as well. The result shows that YOLOv3 is an excellent approach for strawberry maturity classification on both image types.

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches.

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

Design and Implementation of a Hydroponic Strawberry Monitoring and Harvesting Timing Information Supporting System Based on Nano AI-Cloud and IoT-Edge

The strawberry market in South Korea is actually the largest market among horticultural crops. Strawberry cultivation in South Korea changed from field cultivation to facility cultivation in order to increase production. However, the decrease in production manpower due to aging is increasing the demand for the automation of strawberry cultivation. Predicting the harvest of strawberries is an important research topic, as strawberry production requires the most manpower for harvest. In addition, the growing environment has a great influence on strawberry production as hydroponic cultivation of strawberries is increasing. In this paper, we design and implement an integrated system that monitors strawberry hydroponic environmental data and determines when to harvest with the concept of IoT-Edge-AI-Cloud. The proposed monitoring system collects, stores and visualizes strawberry growing environment data. The proposed harvest decision system classifies the strawberry maturity level in images using a deep learning algorithm. The monitoring and analysis results are visualized in an integrated interface, which provides a variety of basic data for strawberry cultivation. Even if the strawberry cultivation area increases, the proposed system can be easily expanded and flexibly based on a virtualized container with the concept of IoT-Edge-AI-Cloud. The monitoring system was verified by monitoring a hydroponic strawberry environment for 4 months. In addition, the harvest decision system was verified using strawberry pictures acquired from Smart Berry Farm.

Information System Prototyping of Strawberry Maturity Stages using Arduino Uno and TCS3200

Strawberry is one of the commodities of subtropical fruits growing in Indonesia. It has a high economic value, attractive appeal in a red and fresh sweet fruit. The color of a strawberry shows its maturity stages, and the strawberry maturity stages affect postharvest quality. The research aimed to design and implement a tool to approve and apply the maturity stages of strawberries using Arduino Uno and display the information on the web. The method included needs analysis and analysis of work, and the design consisted of hardware design and network design. The testing consisted of TCS3200 sensor testing, LCD testing, servo motor testing, and WEB testing. The results of this study found that when the strawberry fruit was declared mature by the TCS3200 sensor as the color detection sensor, the LCD would display the texts, and the servo would open. The system on the WEB would display the total information of the sorted strawberries.

Klasifikasi Kematangan Stroberi Berbasis Segmentasi Warna dengan Metode HSV

Classification of Strawberry Maturity Based on Color Segmentation using HSV Method. Manual fruit maturity classification has many limitations because it is influenced by human subjectivity. Hence, the application of digital image processing and artificial intelligence becomes more effective and efficient. This study aims to create a classification system that automatically divides strawberry maturity into three categories, namely not ripe, half-ripe, and ripe. The process of identifying the level of fruit maturity is based on the color characteristics Red, Green, Blue (RGB) value of the image. The method used for color segmentation is Hue, Saturation, Value (HSV) and for the classification of strawberry maturity using the Multi-Class Support Vector Machine (SVM) algorithm with a Radial Basic Function (RBF) kernel. Strawberry image data was retrieved using the Logitech C920 camera. The dataset consisted of 158 images of strawberries. The results showed that the classification of strawberry maturity using the multi-class SVM algorithm with kernel parameters RBF cost (C) = 10 and gamma (γ) = 10-3 produced the highest accuracy of 97%.

A smart data-driven rapid method to recognize the strawberry maturity

In recent years, there have been many studies on the recognition of strawberry maturity, but there are still problems such as low recognition accuracy and expensive experimental instruments. These factors make their methods difficult for farmers to use. To solve these problems, we developed a fast, non-destructive, accurate and convenient method for strawberry maturity identification using smartphones. In this paper, strawberry maturity is divided into three levels: mature, nearly-mature and immature. Considering the actual strawberry harvest process and postharvest handling, we focus on the differentiation between the mature and the nearly-mature ones to help farmers reduce possible damage in transit and improve profitability. We obtained the images of strawberries with different maturities at 535 nm and 670 nm wavelengths through a smartphone and got absorbance data by image processing based on the region of interest. The absorbance data were used to establish three maturity recognition models—i.e., multivariate linear, multivariate nonlinear and SoftMax regression classifier. The results showed that the multivariate nonlinear model had the highest identification accuracy (which is over 94%) in the greenhouse. Therefore, this method has considerable potential as a means for rapid recognition of strawberry maturity.

NMR-based global metabolomics approach to decipher the metabolic effects of three plant growth regulators on strawberry maturation

Plant growth regulators (PGRs) are commonly used to regulate maturation in strawberry. Despite this, comprehensive assessments of the metabolomic effects of PGRs on strawberry maturation are lacking. In this study, a nuclear magnetic resonance-based approach, combined with multivariate and pathway analysis, was used to evaluate the regulatory effects of gibberellin, forchlorfenuron, and brassinolide, applied at two different maturation stages, on the expression of metabolites in strawberry. The results demonstrated that the PGRs differentially influenced metabolism, whether applied at the same or different maturation stages. Additionally, we also discovered that these different PGRs exhibited some similar metabolic trends when applied at the same growth period. Our findings validate the use of NMR-based metabolomics for identifying subtle changes in the expression of metabolites associated with PGR application.

Brassinosteroid plays a role on pink stage for receptor and transcription factors involved in strawberry fruit ripening

In contrast to climacteric fruits, the ripening regulation of non-climacteric fruits is not well understood. Strawberry is a representative example of this kind of fruit, so it has been used as a model system for this category. In this study, the effect of exogenous brassinosteroid (BR) on the expression of the receptor (FaBRI1) and two components of the signaling pathway (FaBIN2 and FaBRZ1) was analyzed in Fragaria × ananassa cultivar Camino Real by quantitative real-time polymerase chain reaction (RT-qPCR). The physicochemical and phytochemical characteristics of fruits were evaluated in the field and postharvest trials. Perception and signal transduction pathway show little gene action mainly when elicited by epibrassinolide, having treatment differences due mainly the pink stage. This leads us to suggest that BR is involved in strawberry fruit ripening, where the threshold to action seems to be very low and act in the pink stage according to perception and transduction signals. However, owing to the physicochemical and phytochemical characteristics, the BR influence mainly starts in the white stage for total sugar and soluble solid in field assay and for total sugar in the postharvest assays. In addition, there is a positive effect on vitamin C content and total anthocyanins for the treated red fruits in the postharvest assay. All results show that BR is involved in strawberry fruit ripening, in different stages, mainly in a phenylpropanoid pathway. However, new assays to confirm the real BR importance on strawberry maturation and fruit quality.

Direct Assessment of Phytochemicals Inherent in Plant Tissues Using Extractive Electrospray Ionization Mass Spectrometry

An ambient pressure ionization mass spectrometric strategy called internal extractive electrospray ionization mass spectrometry (iEESI-MS) has been developed and applied for direct profiling of labile phytochemicals inherent in various native plant tissues, including leaves, roots, and fruits. By passing the electrospray solvent through the plant tissue, a variety of phytochemicals, such as amino acids, sugars (e.g., glucose, sucrose, polysaccharides, etc.), and alkaloids, were continuously extracted from the sample interior, driven toward the natural/cut electro-spraying tip, and vaporized into gaseous ions for mass spectrometric interrogation. Phytochemical patterns obtained by iEESI-MS permit a rapid differentiation between various species of ginkgo plant and strawberry maturity stages, as well as characterization of physiological/pathologic conditions of chlorophytum comosum. Our experimental results further demonstrate that the established iEESI-MS approach is potentially useful for direct phytochemomics studies with minimal biodegradation, allowing elucidation of plant metabolism with high speed, specificity, and simplicity of analysis.

EFFECT OF TEMPERATURE IN CARPEL MATURATION AND MISSHAPEN FRUIT IN STRAWBERRY

This research was carried out in order to know about the effect of low temperature on strawberry carpels maturation and finally the obtaining of misshapen fruit. Two groups of 20 strawberry plants (cv. 'Camarosa') were put down to temperatures of 22 or 7°C with a photoperiod of 11 h light/13h dark. Then, five flowers at different development stages: 4, 3, 2, and 1 days before anthesis and 1, 2, 3, 4, 5 and 7 days after anthesis were chosen and pollinated. 24 hours later, in each case, carpel maturation was estimated. Results showed differences in the carpel maturation under different culture conditions, and according to carpel position (apical or basal) in the receptacle. In plants put down to 7°C, 100% of the apical stigma of flowers in stage two days before anthesis was immature. In the same phenologic stage, all the apical stigmas of flowers of plants put down to standard temperature (22°C) were basically mature. Different stigma maturation rate between apical and basal carpels was observed. In general, we found that in apical stigma, maturation was slower than in basal stigma. The slowest maturation occurred in apical stigma at 7°C. Percentage of misshapen fruit developed from hand-pollinated flowers varied depending on the flower maturation stage. Pollinated flowers, until one day before antesis stage, tended to produce misshapen fruits.

Influence of light intensity on extracted colour feature values of different maturity in strawberry

Fruit surface features depend on maturity. Based on the analysis of the mature features and their physiological and biochemical value, the maturity standard is different from the traditional method—the red area is over 70% of the whole surface. The degree from light‐red to black‐red in the strawberry surface is put forward to be used to distinguish strawberry maturity in this paper. When the fruit is sorted or harvested in natural conditions, the natural light affects on the fruit extracts colour features. To analyse the influence of light intensity on extracted colour features, a closed box for simulating light was designed. The light intensity was adjusted by the input electric current of an intelligent light‐regulated ballast. Three‐hundred‐and‐sixty pictures of differently mature strawberries in different light intensities were captured, and their extracted colour feature values were recorded. The results show that changing the light intensity from 1215 lx to 3790 lx had little influence on the extracted colour feature value of different strawberry maturity. Three strawberry maturities can be distinguished by three extracted colour feature values, which are the R value in RGB colour model, and a* value and a*2 + b*2 value in L*,a*,b* colour model.