Computers and Electronics in Agriculture最新文献

{"title":"Weed identification in soybean seedling stage based on UAV images and Faster R-CNN","authors":"Jian Cui ,&nbsp;Xinle Zhang ,&nbsp;Jiahuan Zhang ,&nbsp;Yongqi Han ,&nbsp;Hongfu Ai ,&nbsp;Chang Dong ,&nbsp;Huanjun Liu","doi":"10.1016/j.compag.2024.109533","DOIUrl":"10.1016/j.compag.2024.109533","url":null,"abstract":"<div><div>The natural environment in which field soybeans are grown is complex in terms of weed species and distribution, and a wide range of weeds are mixed with soybeans, resulting in low weed recognition rates. Weeds compete with soybeans for sunlight, water and nutrients, and if not managed in a timely manner, weeds may impede soybean growth and reduce yield. The seedling stage is the early stage of soybean growth, and the growth status of soybeans and weeds varies greatly, making it easier to identify and manage weeds. In this paper, a field soybean weed recognition method based on low altitude UAV images and Faster R-CNN algorithm is proposed by utilizing soybean seedling stage weed data collected at low altitude by UAV equipment. A dataset containing 4000 images of soybeans, weeds and broadleaf weeds was constructed and generated in PASCAL VOC format. First, the classification effects of four backbone feature extraction networks, ResNet50, ResNet101, VGG16 and VGG19, were compared to determine the optimal structure; second, the aspect ratio distribution and area distribution of the targets in the dataset were analyzed, and a suitable anchoring framework was designed according to the characteristics of the dataset itself, and the target was trained to be able to recognize soybean seedling weeds with different weed densities. detection model; and two classical target detection algorithms SSD, YOlOv3, YOLOV4, and YOLOV7 were compared. This experiment shows that the Faster R-CNN model with VGG16 as the backbone feature extraction network has the optimal recognition accuracy. By analyzing the characteristics of the dataset itself and optimizing the anchor frame parameters, the optimized model has an average recognition accuracy of 88.69 % for a single data frame, and an average recognition time of 310 ms, which can accurately recognize soybean seedlings and weeds of different densities. Comparing the optimized Faster R-CNN with mainstream target detection models, the average accuracy is 6.31 % higher than the SSD model, 5.79 % higher than the YOlOv3 model, 6.8 % higher than YOLOV4, and 2.92 % higher than YOLOV7. The results show that the optimized target detection model in this paper is more advantageous and can provide scientific guarantee for grass damage monitoring and control in UAV scale.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cattle behavior recognition from accelerometer data: Leveraging in-situ cross-device model learning","authors":"Reza Arablouei ,&nbsp;Greg J. Bishop-Hurley ,&nbsp;Neil Bagnall ,&nbsp;Aaron Ingham","doi":"10.1016/j.compag.2024.109546","DOIUrl":"10.1016/j.compag.2024.109546","url":null,"abstract":"<div><div>Automating livestock behavior recognition using wearable sensors offers significant benefits for monitoring animal health, ensuring welfare, and enhancing farm productivity. While collar-mounted accelerometers provide useful data leading to accurate behavior recognition models, ear-tags offer greater practicality and scalability. However, ear-tag data is affected by independent ear movements (e.g., for flicking flies), necessitating extensive labeled data for accurate recognition, which is time-consuming and costly to obtain. To address this challenge, we propose a pioneering cross-device learning approach. By leveraging a pre-trained behavior recognition model from collar data to guide ear-tag model training, we significantly reduce the need for manual labeling of ear-tag data. This facilitates the development of efficient and scalable behavior recognition models suitable for wider deployment. Additionally, we introduce a novel deep neural network architecture that integrates linearly-constrained convolutional layers specifically designed to counteract gravity’s impact on accelerometer data, along with a confidence penalty term to combat overfitting when training on limited labeled data. Evaluation on real-world cattle data demonstrates that our approach yields ear-tag model performance nearly on par with collar models. This breakthrough paves the way for personalized behavior recognition models using ear-tags, requiring only brief periods of collar-based labeling per animal.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel daily behavior recognition model for cage-reared ducks by improving SPPF and C3 of YOLOv5s","authors":"Gen Zhang ,&nbsp;Chuntao Wang ,&nbsp;Deqin Xiao","doi":"10.1016/j.compag.2024.109580","DOIUrl":"10.1016/j.compag.2024.109580","url":null,"abstract":"<div><div>Intensive duck farming can improve production efficiency and reduce environmental pollution. In modern intensive farming, ensuring the well-being and health of ducks is a paramount concern. Generally, the health status of ducks is determined by monitoring their daily behaviors, such as eating. However, research on cage-reared duck daily behavior recognition is scarce. Therefore, this study proposes a cage-reared duck daily behavior recognition model based on the improved YOLOv5s, denoted DBR-YOLOv5s for notational convenience. Specifically, to tackle the interfered features caused by the duck cage, an improved shrinkage mechanism is introduced in block SPPF of YOLOv5s. To decrease the feature information loss, the convolution operation replaces the maxpool operation in SPPF. Moreover, to cope with the issue of occlusion, the last three C3 blocks in module Neck of YOLOv5s are optimized via the multi-scale convolution operation, promoting the capability of DBR-YOLOv5s to extract contextual information. Extensive experiments were conducted on the self-constructed duck daily behavior dataset. The test precision of the proposed DBR-YOLOv5s is 92.8% for drinking, 96.8% for lying, 93.8% for standing, 98.5% for eating, 89.9% for preening, and 96.7% for spreading. Compared with the state-of-the-art YOLOv8x and YOLOv9c models, the average precision of DBR-YOLOv5s is 1.3% and 2.4% higher, respectively. The results indicate that the proposed DBR-YOLOv5s is effective for cage-reared duck daily behavior recognition, providing a non-contact method for duck daily behavior recognition.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New dielectric-based smart sensor with multi-probe arrays for in-vivo monitoring of trunk water content distribution of a tree in a poplar stand","authors":"Xianglin Cheng ,&nbsp;Xiaotong Wu ,&nbsp;Yufan Zhu ,&nbsp;Yang Zhao ,&nbsp;Benye Xi ,&nbsp;Xiaofei Yan ,&nbsp;Ricardo F. de Oliveirad ,&nbsp;Qiang Cheng","doi":"10.1016/j.compag.2024.109585","DOIUrl":"10.1016/j.compag.2024.109585","url":null,"abstract":"<div><div>Trunk water content (TWC) plays an important role in the study of drought resistance, cold resistance, and precision irrigation of trees. The phloem and xylem are two important tissues for water storage and transport in tree trunks. Currently, sensor techniques can hardly measure the TWC distribution in vivo to characterize the water dynamics of phloem and xylem in the field. In this study, we designed and developed a new smart sensor for in vivo monitoring of TWC distribution in the radial direction of the trunk. The sensor consists of five modules: a dielectric measurement module, multiplexer module, control unit module, multi-dielectric probe array, and multi-resistance probe array. A series of tests were conducted to evaluate the feasibility and performance of the sensor. The multi-dielectric and multi-resistance probe arrays were calibrated. Temperature effects from the sensor circuit and the dielectric properties of the measured medium were corrected. Field observations were conducted in a poplar stand, and a new sensor was inserted into the trunk of a poplar tree. The calibration results showed that the determination coefficients (R²) of the multi-dielectric probe were 0.9622, 0.9543, 0.9566, 0.9428, 0.9658 and 0.9890, and the R² values of the multi-resistance probe calibration results were all higher than 0.99. Field observation results showed that the sensor can monitor the radial distribution of TWC in a poplar tree in vivo. Through the analysis of the hysteresis of TWC between phloem and xylem, we found that the time of water discharge in the phloem was earlier than that in the xylem. Generally, the smart sensor can measure the radial distribution of TWC and has great potential in future applications for tree drought monitoring.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PupaNet: A versatile and efficient silkworm pupae (Bombyx mori) identification tool for sericulture breeding based on near-infrared spectroscopy and deep transfer learning","authors":"Haibo He ,&nbsp;Hua Huang ,&nbsp;Shiping Zhu ,&nbsp;Lunfu Shen ,&nbsp;Zhimei Lv ,&nbsp;Yongkang Luo ,&nbsp;Yichen Wang ,&nbsp;Yuhang Lin ,&nbsp;Liang Gao ,&nbsp;Benhua Xiong ,&nbsp;Fangyin Dai ,&nbsp;Tianfu Zhao","doi":"10.1016/j.compag.2024.109555","DOIUrl":"10.1016/j.compag.2024.109555","url":null,"abstract":"<div><div>Automatic identification of pupal metamorphosis development phases (PMDPs), pupal sex, and pupal species can provide labor-saving and intelligent breeding strategies for sericulture. PupaNet, a one-dimensional convolutional neural network, was developed using near-infrared (NIR) spectra for pupae identification and to assess the reliability of sex identification during PMDPs. Its learning effectiveness was enhanced with the convolution sampling method, attention mechanism, vector normalization method, Mish function, group normalization, improved residual block, and DiffGrad optimizer. To capture the feature pattern of PMDPs, species, and sexes, three datasets were used for testing: Dataset A included 7,200 transmission NIR (T-NIR) spectra of five PMDPs, and Datasets B and C contained 1,920 T-NIR and 1,920 diffuse reflection NIR spectra, each from four species and two sexes. Ablation studies on dataset A identified the PupaNet architecture and the most effective transfer learning parameters. Overall, PupaNet achieved 93.81 % accuracy for PMDP identification and 99.55 % for sex identification during PMDPs using dataset A; 99.84 % for multispecies sex identification, 98.24 % for species identification, and 97.71 % for species and sex identification with dataset B; and 98.83 % for multispecies sex identification, 95.99 % for species identification, and 94.11 % for species and sex identification using dataset C. All these identifications featured areas under the receiver operating characteristic curves above 0.99 with an inference time of 3.65 ms. Moreover, the sample feature space and key wavelengths identified by PupaNet for a specific class were visualized. These findings demonstrate that PupaNet is a versatile and efficient tool for pupae identification and has the potential to advance sericulture breeding.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"European beekeepers’ interest in digital monitoring technology adoption for improved beehive management","authors":"Wim Verbeke ,&nbsp;Mariam Amadou Diallo ,&nbsp;Coby van Dooremalen ,&nbsp;Marten Schoonman ,&nbsp;James H. Williams ,&nbsp;Marie Van Espen ,&nbsp;Marijke D’Haese ,&nbsp;Dirk C. de Graaf","doi":"10.1016/j.compag.2024.109556","DOIUrl":"10.1016/j.compag.2024.109556","url":null,"abstract":"<div><div>This study investigates the adoption of Digital Beehive Monitoring Technology (DBMT) based on a survey with 844 beekeepers across 18 European countries, shedding light on their characteristics, current usage patterns, expected benefits, and the determinants influencing technology adoption. Notably, 79.1 % of beekeepers had yet to embrace any form of digital monitoring, while 20.9 % engaged in limited monitoring, primarily focused on hive weight. The perceived benefits of DBMT were explored, with hive management facilitation, colony health enhancement, winter loss reduction, and time-saving emerging as primary expectations. A quarter of beekeepers expressed uncertainty regarding these anticipated benefits, underscoring the need for increased awareness and education about the advantages of DBMT. Logistic regression is used to uncover key determinants influencing DBMT adoption, emphasizing the role of professionalism, regional disparities, and active participation in beekeepers’ associations. The application of the Theory of Planned Behaviour (TPB) through Structural Equation Modelling reinforced the central role of beekeepers’ personal attitudes in shaping their intention to adopt DBMT, with social norms and perceived behavioural control providing complementary albeit minor influences. The findings imply that hobbyist beekeepers may be more involved in beekeeping as a nature-centric activity, whereas professional beekeepers demonstrate a greater inclination toward digitalisation. With the so-called social tipping point of 25 % for technology adoption being almost reached, this study provides a timely empirical perspective on the European beekeeping sector’s evolution towards digitalisation, so-called Apiculture 4.0.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying rice lodging based on semantic segmentation architecture optimization with UAV remote sensing imaging","authors":"Panli Zhang ,&nbsp;Sheng Zhang ,&nbsp;Jiquan Wang ,&nbsp;Xiaobo Sun","doi":"10.1016/j.compag.2024.109570","DOIUrl":"10.1016/j.compag.2024.109570","url":null,"abstract":"<div><div>Lodging, a prevalent issue during rice growth, detrimentally impacts both yield and quality. It also complicates the harvesting process, reducing the efficiency of mechanized collection. Existing monitoring methods, predominantly based on manual observation and satellite remote sensing, fall short in addressing the requirements of contemporary, efficient, and real-time agriculture. This research integrates image analysis techniques with advanced optimization algorithms to develop a semantic segmentation model specifically designed for detecting rice lodging in remote sensing images. The model, named MI-UConvNeXt, employs a ConvNeXt-based feature extraction network utilizing UNet architecture (UConvNeXt) and incorporates an improved multi-objective salp swarm algorithm with Latin hypercube sampling and an elite opposition-based learning strategy (ISSA-LE) to dynamically adjusting the number of UConvNeXt channels. MI-UConvNeXt achieves a balance between accuracy and complexity. Compared to seven other semantic segmentation models from the literature, MI-UConvNeXt exhibits enhanced performance, with a Pixel Accuracy (<em>PA</em>) of 95.59%, mean Pixel Accuracy (<em>mPA</em>) of 95.62%, and mean Intersection over Union (<em>mIoU</em>) of 91.91% on the validation set. This demonstrates the model’s superior accuracy, lower computational resource demands, and enhanced efficiency. By integrating deep learning with intelligent optimization algorithms, this study offers a novel and effective approach for monitoring crop lodging in agricultural production, providing robust technical support for the accurate extraction of crop phenotypic information.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EVIT-YOLOv8: Construction and research on African Swine Fever facial expression recognition","authors":"Lili Nie ,&nbsp;Bugao Li ,&nbsp;Fan Jiao ,&nbsp;Wenjuan Lu ,&nbsp;Xinlong Shi ,&nbsp;Xinyue Song ,&nbsp;Zeya Shi ,&nbsp;Tingting Yang ,&nbsp;Yihan Du ,&nbsp;Zhenyu Liu","doi":"10.1016/j.compag.2024.109575","DOIUrl":"10.1016/j.compag.2024.109575","url":null,"abstract":"<div><div>The threats of infectious diseases such as African Swine Fever, Swine Erysipelas, and Blue Ear Disease to the pig farming industry have been increasing year by year. Producers often face difficulties in diagnosis, leading to the misuse of measures and the spread of epidemics. Addressing this, the proposed EVIT-YOLOv8 model integrates the EViT module to surpass ViT limitations and incorporates the CBAM module for enhanced image feature representation. Employing the GIOU loss function ensures better precision in capturing facial expression features, yielding an impressive Mean Average Precision (mAP) of 86.6% in differentiation tasks. Specifically, in African Swine Fever facial expression recognition, the model achieves a remarkable Precision of 85.2%, outperforming YOLOv5, YOLOv7, and YOLOv8 models by 6%, 23.5%, and 7.3%, respectively. This provides pig producers with a precise diagnostic tool, mitigating the risk of epidemic spread due to misdiagnosis and facilitating effective prevention and control of infectious diseases.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the double-coil heat exchanger for dehumidification and heating in greenhouses: Modeling, analysis, and optimization","authors":"Chengji Zong ,&nbsp;He Li ,&nbsp;Weitang Song ,&nbsp;Shumei Zhao ,&nbsp;Jiarui Lu ,&nbsp;Qiangwei Ma","doi":"10.1016/j.compag.2024.109569","DOIUrl":"10.1016/j.compag.2024.109569","url":null,"abstract":"<div><div>Heat exchangers are widely used in dehumidification and heating technologies owing to their excellent heat transfer performance, however, their dehumidification capability is limited for greenhouse applications. This study evaluates a double-coil heat exchanger (DC-HE) for dehumidification and heating in greenhouses. Humid air undergoes condensation dehumidification through the dehumidifying coil and is then reheated by the heating coil before being released. Using double coils allows for the separate circulation of cold and warm water in their respective paths, handling the latent and sensible heat of the air. In this study, predictive methods for air treatment in a heat exchanger are combined, and theoretical calculations are performed on the DC-HE and validated through experimental investigations. Furthermore, the impact of variables such as air temperature, relative humidity, water temperature and flow rate, and air flow rate on the moisture removal rate (MRR), latent heat ratio (LHR), and heating capacity (<em>q_heat</em>) of the DC-HE is analyzed, the effects of these variables on the sensible and latent heat transfer rates are discussed. Finally, the optimal variables for maximizing MRR and LHR are determined using the response surface method and multi-objective genetic algorithm. When the inlet air temperature, relative humidity, cooling-side water temperature, heating-side water temperature, cooling-side water flow rate, heating-side water flow rate, and air flow rate are 11.8 ℃, 95 %, 3 ℃, 30 ℃, 2 m/s, 2 m/s, and 2 m/s, respectively, the DC-HE achieves an MRR of 1.58 g/s, an LHR of 62.2 % and a <em>q_heat</em> of 9.99 kW. Compared with other finned tube heat exchangers, the DC-HE enhances the moisture removal capacity and significantly reduces the temperature requirement for the heat source, offering a promising dehumidification and heating technology for greenhouse applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AgRegNet: A deep regression network for flower and fruit density estimation, localization, and counting in orchards","authors":"Uddhav Bhattarai,&nbsp;Santosh Bhusal,&nbsp;Qin Zhang,&nbsp;Manoj Karkee","doi":"10.1016/j.compag.2024.109534","DOIUrl":"10.1016/j.compag.2024.109534","url":null,"abstract":"<div><div>One of the major challenges for the agricultural industry today is the uncertainty in manual labor availability and the associated cost. Automated flower and fruit density estimation, localization, and counting could help streamline harvesting, yield estimation, and crop-load management strategies such as flower and fruitlet thinning. This article proposes a deep regression-based network, AgRegNet, to estimate density, count, and location of flower and fruit in tree fruit canopies without explicit detection or polygon annotation. Inspired by popular U-Net architecture, AgRegNet is a U-shaped network with an encoder-to-decoder skip connection and modified ConvNeXt-T as an encoder feature extractor. AgRegNet can be trained based on information from point annotation and leverages segmentation information and attention modules (spatial and channel) to highlight relevant flower and fruit features while suppressing non-relevant background features. Experimental evaluation in apple flower and fruit canopy images under an unstructured orchard environment showed that AgRegNet achieved promising accuracy as measured by Structural Similarity Index (SSIM), percentage Mean Absolute Error (pMAE) and mean Average Precision (mAP) to estimate flower and fruit density, count, and centroid location, respectively. Specifically, the SSIM, pMAE, and mAP values for flower images were 0.938, 13.7%, and 0.81, respectively. For fruit images, the corresponding values were 0.910, 5.6%, and 0.93. Since the proposed approach relies on information from point annotation, it is suitable for sparsely and densely located objects. This simplified technique will be highly applicable for growers to accurately estimate yields and decide on optimal chemical and mechanical flower thinning practices.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}