Computers and Electronics in Agriculture最新文献

{"title":"Multisite evaluation of microtensiometer and osmotic cell stem water potential sensors in almond orchards","authors":"","doi":"10.1016/j.compag.2024.109547","DOIUrl":"10.1016/j.compag.2024.109547","url":null,"abstract":"<div><div>In the face of climate change, optimization of almond irrigation management is critical for ensuring the long-term sustainability of nut production and water resources. To achieve optimal irrigation management, continuous monitoring of the plant water status is critical in scheduling irrigation. It is a widely accepted practice to use stem water potential (SWP) as a measure of plant water status in woody perennials like almonds. However, the pressure chamber (PC) commonly used to make these measurements is labor-intensive and does not provide continuous data without significant additional labor. In this study, we evaluated two recently developed stem water potential sensors (Microtensiometer [MT], and Osmotic Cell [OC]), both of which can measure the SWP nearly continuously when embedded in stem sapwood tissue (typically in the trunk or branch of a tree). SWP sensors were evaluated in nine commercial almond orchards in the Central Valley of California. The SWP values obtained from both sensors were compared to the values measured using a PC using statistical software called FITEVAL. Overall, sensor performance varied from good to acceptable and from acceptable to unacceptable for MT and OC sensors respectively. The MT sensors demonstrated higher accuracy with a Nash-Sutcliff Coefficient of Efficiency (NSE) of 0.84 (95 % CI: 0.78–0.88) and a Root Mean Square Error (RMSE) of −0.24 MPa (95 % CI: −0.21 to −0.28 MPa), while the OC sensor had an NSE of 0.68 (95 % CI: 0.61–0.74) and an RMSE of −0.32 MPa (95 % CI: −0.29 to −0.35 MPa). MT sensors exhibited the added advantage of providing sub-hourly data and displaying tree recovery from water stress following irrigation, positioning them as potentially superior for precision almond orchard water management. If widely adopted, SWP sensors have the potential to optimize water use in almond production.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593815","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":"Review of weed recognition: A global agriculture perspective","authors":"","doi":"10.1016/j.compag.2024.109499","DOIUrl":"10.1016/j.compag.2024.109499","url":null,"abstract":"<div><div>Recent years have seen the emergence of various precision weed management technologies in both research and commercial contexts. These technologies better target weed management interventions to provide weed control that is more efficient and environmentally friendly. To support this effort, a significant amount of research has focused on machine vision to recognize weeds in a variety of crops. In this work, we systematically survey recent literature on weed recognition in crops and evaluate its relevance based on the status of global agriculture as presented in FAO statistics. Our findings indicate a notable emphasis on crops like sugar beet, carrot, and maize, while wheat and rice, despite their substantial contribution to global cropland and food supply, are relatively understudied. We conduct an in-depth analysis of the 12 most researched crop categories to discern trends in weed recognition research, and to understand why some crops are studied more intensively than others. This analysis reveals that the trajectory of research varies significantly between crops. We find that weed recognition in some globally critical crops is at an early stage of development, and lacks implementation and testing in real-world environments. Additionally, we find the differences in approach to weed recognition are not explained solely by the requirements of precision weed management for a given crop. Instead, the approaches taken, like with the choice of crop, often appear expedient, influenced by factors such as readily available annotated data, rather than by the crop-specific requirements of a precision weed management system.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594113","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 crop’s spectral signature is worth a compressive text","authors":"","doi":"10.1016/j.compag.2024.109576","DOIUrl":"10.1016/j.compag.2024.109576","url":null,"abstract":"<div><div>The accuracy of crop mapping based on remotely sensed hyperspectral imagery has been significantly improved through the use of deep learning. However, traditional deep learning can be computationally intensive, requiring millions of parameters, which can make it ‘expensive’ to deploy and optimize. Inspired by studies in natural language processing, we consider the spectral signature corresponding to each pixel as text. Specifically, we first feed the hyperspectral image (HSI) data into the Channel2Vec module to generate channel embeddings. Based on the channel embeddings, we use a lossless compressor and Normalized Compression Distance (NCD) to create a spectral tokenizer. It can segment the spectral signature corresponding to each pixel into multiple windows along the channel dimension, and then extract local sequence information from each window. By combining the local sequence information with the original HSI data, we construct spectral embeddings. Finally, we again use the lossless compressor to compute the NCD between the spectral embeddings, and then classify using only the <span><math><mi>k</mi></math></span>-nearest-neighbor classifier (<span><math><mi>k</mi></math></span>NN). The proposed framework is ready-to-use and lightweight. Without any training, it achieves results competitive with deep learning models on three benchmark datasets. It outperforms the average of 11 advanced deep learning methods trained at scale. Moreover, it outperforms more than half of these models in the few-shot scenario, where there are not enough labels to effectively train a neural network.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578238","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":"Upscaling and downscaling approaches for early season rice yield prediction using Sentinel-2 and machine learning for precision nitrogen fertilisation","authors":"","doi":"10.1016/j.compag.2024.109603","DOIUrl":"10.1016/j.compag.2024.109603","url":null,"abstract":"<div><div>Early season yield prediction could support rice farmers in adopting precision agriculture for nitrogen fertilisation management. Remote sensing and machine learning (ML) can be used to predict and map crop yield during phenological stages relevant to nitrogen application, like tillering in rice, at both within-field and field scales. This study evaluated the transferability of ML models in early season yield prediction through upscaling and downscaling approaches. The effects of two prediction times (tillering and ripening stages) and training/testing set sizes on ML models performance were evaluated over five rice growing seasons (from 2018 to 2022) in northern Italy, using whole-field-average yields and yield maps. Vegetation indices from Sentinel-2 imagery using the Google Earth Engine platform fed five ML algorithms (Cubist-CUB, Gaussian Process Regression-GPR, Neural Network-NNET, Random Forest-RF, and Support Vector Machines-SVM). ML algorithms were trained with yield maps and tested with whole-field-average yields to obtain a downscaling approach, while the opposite was done to obtain an upscaling approach. The downscaling approach showed higher accuracy than upscaling approach. Ripening stage predictions were more accurate than tillering stages, although the downscaling approach showed small differences between tillering and ripening stages. The highest tillering stage accuracy was achieved by SVM for both downscaling and upscaling approaches with 20 % and 27.8 % of Normalized Root Mean Square Error (NRMSE), and 0.99 and 0.99 of Simple Additive Weighting (SAW) score, respectively. Set size and data distribution effected ML models accuracy, with the highest performance achieved by RF and GPR with 0.80 and 1.00 of SAW score for the downscaling and upscaling approaches, respectively. This study demonstrated how ML models and downscaling approach could support rice farmers to calculate the nitrogen dose using the predicted yield at the tillering stage, enabling them to apply a site-specific nitrogen fertilisation based on the within-field yield prediction variability.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571282","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":"Capped honey segmentation in honey combs based on deep learning approach","authors":"","doi":"10.1016/j.compag.2024.109573","DOIUrl":"10.1016/j.compag.2024.109573","url":null,"abstract":"<div><div>Honey is the food stored by honey bees for periods when it is scarce in the field as well as being a product that is consumed worldwide by humans. Each hive generates different amounts of honey depending on the population of the bee hive, health state or environmental factors. In fact, the reserves of honey provide beekeepers with a double function: to predict the amount of honey that can be obtained and to analyze the state of the bee colonies. The assessment of honey reserves is commonplace in scientific research related to the health of bee colonies, genetic improvement or environmental issues, and emerging technologies can provide useful tools to evaluate honey stored in hives. In this context, this work proposes a methodology to detect the honey areas in high resolution photographs automatically using methods based on deep learning. Specifically, the methodology follows a <em>“divide and conquer”</em> approach where the images are separated into tiles with overlapping areas that are used by a semantic segmentation algorithm based on Feature Pyramid Network (FPN), detecting the honey in each tile to finally merge the tiles back into the complete image. The proposal has been compared with different feature extractors (backbones) and other semantic segmentation models, obtaining on average accurate results above 90% and 87% in the Dice score and IOU metrics respectively.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571484","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":"Multi-modal machine learning for the early detection of metabolic disorder in dairy cows using a cloud computing framework","authors":"","doi":"10.1016/j.compag.2024.109563","DOIUrl":"10.1016/j.compag.2024.109563","url":null,"abstract":"<div><div>In precision livestock farming (PLF), wearable sensors, computer vision, and genomic tests generate large amounts of data, which can be challenging to integrate and analyze jointly due to their diverse nature. However, incorporating both genomic and phenotypic data together can be beneficial for developing predictive models in animal biology. The development of automated and modular data pipelines using scalable solutions such as cloud computing can be an effective strategy to integrate and analyze animal-level information in real-time. The objectives of this study were (1) to propose a cloud computing-based framework to automate the processing and integration of phenotypic and genotypic data, and (2) to assess different data fusion strategies (early and late fusion, and cooperative learning) for the early detection of subclinical ketosis (SCK) in dairy cows, integrating wearable sensors, imaging systems, and genotypic data in livestock farms. We developed a modular pipeline for image analysis, which includes body segmentation, frame quality assessment, animal identification, and body condition score (BCS), which were crucial for producing the features used in SCK detection. The body segmentation module achieved a Dice similarity coefficient of 0.990, the frame quality assessment module reached 99.1 % accuracy, the animal identification module attained 93.2 % accuracy, and the BCS module achieved accuracies of 81.1 % and 96.2 % when allowing up to 0.25 and 0.50 prediction error, respectively. For SCK detection, early fusion and cooperative learning achieved the lowest mean absolute errors in predicting plasma beta-hydroxybutyrate as a continuous variable (as low as 0.242). Late fusion, combined with an ordinary least squares regression, achieved the highest F₁ scores for binary SCK prediction (up to 0.750). These results suggest that data fusion techniques can be effectively used to integrate genotypic and phenotypic data from multiple sensors. Additionally, SCK detection can be performed on dairy farms using the proposed cloud computing-based framework, which was implemented with modular, independent services that can be customized and reused for a variety of tasks.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571485","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":"Ground-based on-line weed control using computer vision: Analyzing the inference time-accuracy dilemma","authors":"","doi":"10.1016/j.compag.2024.109577","DOIUrl":"10.1016/j.compag.2024.109577","url":null,"abstract":"<div><div>The detrimental effects of weeds on crop growth and yield present substantial challenges to the agribusiness sector, necessitating the deployment of robust control strategies. The rapid advancement of Computer vision (CV) techniques has driven the integration of ground-based imaging sensors to enable site-specific weed management. The main challenge in weed management revolves around on-line weed detection, which demands a careful balance between inference time and detection accuracy. Finding this balance is very important, as prioritizing a higher number of frames per second (fps) might reduce the detection precision. However, the real-time constraint for on-line weed control remains relatively unexplored. This paper addresses this gap by categorizing proposed approaches based on ground-vehicle configuration and evaluating the real-time requirements for on-line weed control. We comprehensively examine the different components of ground-vehicles including the travel speed, camera settings, and weeding tools to understand the fps required for seamless weed control operation. Results show that for travel speeds below <span><math><mrow><mn>4</mn><mspace></mspace><mi>k</mi><mi>m</mi><mspace></mspace><msup><mrow><mi>h</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>, Deep Neural Networks (DNNs) operating at fps rates lower than 10 Hz are suitable for effective on-line weed detection. However, at higher speeds or with smaller Fields of View, fps demands increase. Our findings further reveal that the relatively relaxed fps requirements of on-line weed control create opportunities for deploying larger DNNs, such as NASNet-A-Large, which can significantly enhance detection accuracy. The operational latency introduced by certain weeding tools further provides additional processing time for DNNs. The continuous advancement of larger DNNs and improvements in hardware offer promising prospects for precise and effective weed management. Future research should leverage these developments, focusing on enhancing detection accuracy rather than optimizing for faster inference times, given the relaxed real-time constraints of ground-based weed control.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571486","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":"Tea bud DG: A lightweight tea bud detection model based on dynamic detection head and adaptive loss function","authors":"","doi":"10.1016/j.compag.2024.109522","DOIUrl":"10.1016/j.compag.2024.109522","url":null,"abstract":"<div><div>Tea bud detection plays a crucial role in early-stage tea production estimation and robotic harvesting, significantly advancing the integration of computer vision and agriculture. Currently, tea bud detection faces several challenges such as reduced accuracy due to high background similarity, and the large size and parameter count of the models, which hinder deployment on mobile devices. To address these issues, this study introduces the lightweight Tea Bud DG model, characterized by the following features: 1) The model employs a Dynamic Head (DyHead), which enhances tea bud feature extraction through three types of perceptual attention mechanisms—scale, spatial, and task awareness. Scale awareness enables the model to adapt to objects of varying sizes; spatial awareness focuses on discriminative regions to distinguish tea buds against complex backgrounds; task awareness optimizes feature channels for specific tasks, such as classification or localization of tea buds. 2) A lightweight C3ghost module is designed, initially generating basic feature maps with fewer filters, followed by simple linear operations (e.g., translation or rotation) to create additional “ghost” feature maps, thus reducing the parameter count and model size, facilitating deployment on lightweight mobile devices. 3) By introducing the α-CIoU loss function with the parameter α, the loss and gradient of objects with different IoU scores can be adaptively reweighted by adjusting the α parameter. This approach emphasizes objects with higher IoU, enhancing the ability to identify tea buds in environments with high background similarity. The use of α-CIoU focuses on accurately differentiating tea buds from surrounding leaves, improving detection performance. The experimental results show that compared with YOLOv5s, the Tea Bud DG model reduces the model size by 31.41 % and the number of parameters by 32.21 %. Compared with YOLOv7_tiny, the size and parameters are reduced by 18.94 % and 23.84 %, respectively. It achieved improvements in [email protected] by 3 %, 3.9 %, and 5.1 %, and in [email protected]_0.95 by 2.6 %, 3.2 %, and 4 % compared with YOLOv5s, YOLOv8s, and YOLOv9s, respectively. The Tea Bud DG model estimates the tea yield with an error range of 10 % to 16 %, providing valuable data support for tea plantation management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571283","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":"A grapevine trunks and intra-plant weeds segmentation method based on improved Deeplabv3 Plus","authors":"","doi":"10.1016/j.compag.2024.109568","DOIUrl":"10.1016/j.compag.2024.109568","url":null,"abstract":"<div><div>Accurate identification of grapevine trunks and interplant weeds is crucial for the intelligent development of weeding sessions in vineyards. Challenges arise due to the nonuniform planting of wine grapes, obscuration of grapevine trunks by interplant weeds, and variations in trunk characteristics across different growth stages, complicating the accurate segmentation of grapevine trunks and intraplant weeds. This study presents a new identification model that employs an improved Deeplabv3 Plus framework with lightweight Mobilenetv2 as its central network, supplemented by a coordinate attention block to boost feature extraction capabilities. The model was deployed using the robot operating system (ROS) on a crawler robot for field operations. We developed datasets for grapevine trunks and intraplant weeds, and upon training and testing, the model achieved a mean intersection over union (MIoU) of 84.4 % and a pixel accuracy of 92.03 %. Field trials integrating the ROS system demonstrated a grapevine trunk miss detection rate of 3.6 %, a false detection rate of 2.4 %, and a detection speed of 22 frames per second (FPS). The results show that our method effectively balances recognition accuracy and speed, offering valuable technical support for developing intelligent field weeders for wine grape cultivation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552658","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":"Determination and quantitative evaluation of early postharvest hidden damage in fresh strawberry fruit based on coupling of dynamic finite element method and response surface methodology","authors":"","doi":"10.1016/j.compag.2024.109588","DOIUrl":"10.1016/j.compag.2024.109588","url":null,"abstract":"<div><div>Collision damage is the most common type of damage during the mechanized harvesting, stacking and transportation of strawberries. Aiming at the problem that hidden bruises on fruits caused by collision behaviors are difficult to detect and accurately quantify in the early stage of damage, this paper carried out simulation and experimental research on the bruise susceptibility of strawberry fruits at the moment of collision based on dynamic finite element method and response surface method. By measuring the physical characteristics parameters of three different varieties of strawberry fruits, the three-dimensional solid model of the fruit including cortex, central pith, and achene was established. The multi-scale finite element model of the fruit was further established based on the mechanical property parameters obtained based on the quasi-static compression experiment. A total of 240 different experimental scenarios were set up in this paper, and the cloud diagrams of fruit’s equivalent stress and the changing law of system energy under different conditions were obtained. The experimental results showed that the equivalent stress and contact force inside the fruit vary due to differences in contact material, drop height and impact angle. In order to further obtain the comprehensive effects of contact material, drop height and impact angle on bruise susceptibility, four empirical models for predicting bruise susceptibility were established by using response surface methodology. By comparing the experimental results with the predicted results of the model, it was found that under the conditions where the contact material was steel and the drop height was 1 m, the relative error between the measured value and the predicted value was the smallest (1.38 %) when the impact angle was −19°; the relative error between the measured value and the predicted value was the largest (6.43 %) when the impact angle was −31°. The results of this study showed that the predicting models of strawberry fruit’s bruise susceptibility based on response surface methodology were reasonable and correct. These models can be used to determine the potential mechanical damage of strawberry fruits during mechanized harvesting, stacking and transportation, and can provide a basis for the development of end-effectors/manipulators in strawberry or other fruit and vegetable picking robots, the formulation of picking/harvesting strategies, and the design of packaging container structures.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561128","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}