1,720,977 research outputs found
Latest advances in sensor applications in agriculture
Get PDFSensor applications are impacting the everyday objects that enhance human life quality. In this special issue, the main objective was to address recent advances of sensor applications in agriculture covering a wide range of topics in this field. A total of 14 articles were published in this special issue where nine of them were research articles, two review articles and two technical notes. The main topics were soil and plant sensing, farm management and post-harvest application. Soil-sensing topics include monitoring soil moisture content, drain pipes and topsoil movement during the harrowing process while plant-sensing topics include evaluating spray drift in vineyards, thermography applications for winter wheat and tree health assessment and remote-sensing applications as well. Furthermore, farm management contributions include food systems digitalization and using archived data from plowing operations, and one article in post-harvest application in sunflower seeds
Monitoring within-field variability of corn yield using sentinel-2 and machine learning techniques
Get PDFMonitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4-R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4-R6)
wGrapeUNIPD-DL: An open dataset for white grape bunch detection
Get PDFNational and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset
Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms
Get PDFOver the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE
Comparing maize leaf area index retrieval from aerial hyperspectral images through radiative transfer model inversion and machine learning techniques
No full textThis study compares maize leaf area index (LAI) retrieval methods based on radiative transfer models and machine learning techniques. Ground LAI was measured from the study field at different growth stages where aerial hyperspectral images were acquired at the same stages. The PROSAIL-based model was built using a range of maize leaf and canopy parameters with a total of >21k simulations covering different maize spectral reflectance scenarios. Moreover, random forest and support vector machines were applied to the spectral and corresponding ground LAI measurements divided into 50% for model training and 50% for validation. Results showed that the PROSAIL-based model provided the highest R2 value between ground and estimated LAI followed by the RF and SVM where R2 values were 0.65, 0.59 and 0.35 respectively
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Grape yield spatial variability assessment using YOLOv4 object detection algorithm
No full textSingle shoot detection algorithms represent a promising tool for real-time application of deep learning models. YOLO (You Only Look Once) is a single shoot object detection algorithm that combines fast classification and good accuracy. Over the last few years, several versions of YOLO have been developed, improving its performance. In this study, the last version of YOLO (version 4) was evaluated in its full-size model and in the tiny version, in order to assess grape yield spatial variability. The tiny and full models were previously trained and tested on almost 3000 images collected during several growing stages in different vineyards and varieties. YOLO models were used to classify 24 georeferenced RGB images acquired before the harvesting on an 8-hectare experimental vineyard, where Vitis vinifera cv. Glera vines were trained to Sylvoz and characterized by spatially structured variability. The models were used to detect the number of bunches, based on different resolution images (from 320 up to 1280 pixels) and different confidence thresholds (from 0.25 up to 0.35). The detected number of bunches was then compared with the actual one as well as with the relative final weight harvested from the vines used as target for the collected images.
According to preliminary results, the number of bunches detected in high-resolution images exhibited a higher correlation with the number of bunches visible in the images rather than with the final weight. On the other hand, the number of bunches detected in low-resolution images gave evidence of a higher correlation with the total weight of grapes harvested from the target vines. Although high-resolution images allowed the model to detect almost all bunches not covered by leaves, in low-resolution images YOLO models were weakly affected by small bunches, which were rarely detected, thus increasing the correlation with the vines yield. The best linear regression model for vines yield was obtained with 416 pixels images, which showed a coefficient of determination (R2) of 0.59, indicating YOLO as a suitable tool for detecting yield spatial variability. The models used in this work represent non-destructive methodologies for grape yield spatial variability assessment, and they may be easily implemented as on-the-go tools
Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy
No full textFarmer's management decisions and environmental factors are the main drivers for field spatial and temporal yield variability. In this study, a 22 ha field cultivated with corn for more than ten years using different prescription maps of nitrogen application rates was investigated. Prescription maps were developed based on archived yield maps, soil analysis and recently integrated with Sentinel 2 satellite images. In addition, farmer experience and availability of variable rate application (VRA) requirements had an influence on the development of the homogeneous management zones. The initial approach with VRA was quite simple, based on a simple partitioning of the field into three rectangular zones (defined mainly based on previous yield maps and farmer experience). The partitioning changed with time and knowledge, evolving to the final five irregularly shaped zones (defined based on Farm works decision support software). Furthermore, since 2010 the farmer began using soil moisture sensor for irrigation decisions. Results of the present study highlight an improvement in corn yield and a reduction in total applied nitrogen. Corn yield improved on average by 31% on a ten years basis to reach more than 14 ton/ha dm. in 2018. At the beginning of VRA, yield maps showed a high spatial variation between field zones compared to reduced variation in the following seasons. In addition, the nitrogen applied reduced by around 23% while the total yield was improving. These results showed an increase in the partial factor productivity from less than 54 to around 87 kg of corn grain per kg of nitrogen applied. This promising result shows that farmer management decisions can improve every season by continuous monitoring of crop performance, understanding field variability and taking advantage of recently developed decision support software tools
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