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Headland and field edges have a higher traffic frequency compared to the field centre, which causes more compaction. Most repeated compaction is located at the field entrance area and headland during machinery turning and material transporting that takes place during the fertilisation, herbicide laying, and harvesting of fields, which could cause soil structure destruction and yield reduction. In this study, the differences between headland, field edges, and field centre were studied using yield maps and the vegetation indices (VIs) calculated by the Google Earth Engine (GEE). First, thirteen yield maps from 2019 to 2022 were used to measure the yield difference between headland, field edges, and field centre. Then, one hundred and eleven fields from northern Italy were used to compare the vegetation indices (VIs) differences between headland, field edges, and field centre area. Then, field size, sand, and clay content were calculated and estimated from GEE. The yield map showed that headland and field edges were 12.20% and 2.49% lower than the field centre. The results of the comparison of the VIs showed that headlands and field edges had lower values compared to the field centre, with reductions of 4.27% and 2.70% in the normalised difference vegetation index (NDVI), 4.17% and 2.67% in the green normalized difference vegetation index (GNDVI), and 5.87% and 3.59% in the normalised difference red edge (NDRE). Additionally, the results indicated that the yield losses in the headland and field edges increased as the clay content increased and sand content decreased. These findings suggest that soil compaction and structural damage caused by the higher traffic frequency in the headland and field edges negatively affect crop yield.

National 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.

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R

Over 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.

[This corrects the article DOI: 10.1371/journal.pone.0192830.].

In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants., {"references":["Gauri, S., Safiatou, A. N. and Mohamed, Y. S. \"Renewable Readiness\nAssessement: Djibouti\", International Renewable Energy Agency, IRENA\n(2015).","Abdourazak, A. K., Abderafi, S., Zejli, D. and Ibrahim, I. A.\n\"Potentialities of using linear Fresnel technology for solar energy\ndevelopment in Djibouti\", In Renewable and Sustainable Energy\nConference. IRSEC 2014, IEEE International, 2014, pp. 672-675, IEEE.","Koca, A., Oztop, H. F., Varol, Y. and Koca, G. O. \"Estimation of solar\nirradiation using artificial neural networks with different input parameters\nfor Mediterranean region of Anatolia in Turkey\", Expert Systems with\nApplications, 38, 2011, pp. 8756-8762.","Fadare, D. A. \"Modelling of solar energy potential in Nigeria using\nan artificial neural network model\", Applied Energy, 86, 2009, pp.\n1410-1422.","Dorvloa, A. S. S., Jervase, J. A. and Al-Lawati, A. \"Solar irradiation\nestimation using artificial neural networks\", Applied Energy, 71, 2002,\npp. 307-319.","Amit, K. Y. and Chandel, S. S. \"Solar radiation prediction using Artificial\nNeural Network techniques: A review\", Renewable and Sustainable\nEnergy Reviews, 33, 2014, pp. 772-781.","Md. Shafiqul, I., Md. Monirul, K. and Nafis, K. \"Artificial Neural\nNetworks based Prediction of Insolation on Horizontal Surfaces for\nBangladesh\", Procedia Technology, 10, 2013, pp. 482-491.","Ermis, K., Midilli, A., Dincer, I. and Rosen, M. A. \"Artificial neural\nnetwork analysis of world green energy use\", Energy Policy, 35, 2007,\npp. 1731-1743.","GPS Coordinates and Google Map, \"coordonnees-gps.fr/\". Accessed on\n12/2015.\n[10] Solar and Wind Energy Resource Assessment (SWERA), DNI DLR\n(Germain Aerospace Centre) high Resolution, \"maps.nrel.gov/swera\".\nAccessed on 12/2015.[11] Ouammi, A., Zejli, D., Dagdougui, H. and Benchrifa, R. \"Artificial\nneural network analysis of Moroccan solar potential\", Renewable and\nSustainable Energy Reviews, 16(7), 2012, pp. 4876-4889.\n[12] Mubiri, J. \"Predicting total solar irradiation values using artificial neural\nnetworks\", Renewable Energy, 33(10), 2008, pp. 2329-2332.\n[13] Ouammi, A., Sacile, R., Zejli, D., Mimet, A. and Benchrifa, R.\n\"Sustainability of a wind power plant: application to different Moroccan\nsites\", Energy, 35, 2010, pp. 4226-4236.\n[14] Adnan S., Erol A. and Mehmet O. \"Estimation of solar potential\nin Turkey by artificial neural networks using meteorological and\ngeographical data\", Energy Conversion and Management, 45(18-19),\n2004, pp. 3033-3052.\n[15] Fernando R. M., Enio B. P. and Ricardo A. G. \"Solar radiation\nforecastion using Artificial Neural Networks\", International Journal of\nEnergy Science, 2(6), 2012, pp. 217-227."]}

In this paper, our work focuses on the mains potentialities of the introduction and using linear Fresnel technology for concentrating solar power (CSP) in Djibouti according to its development plan of renewable energies. The solar potential of Djibouti and also the aim of the country to exploit 100% green energy in 2020 are the starting point of this work. The Linear Fresnel Collector technology for CSP seems to be one of the more interesting technologies for Djibouti due to the expected cost advantages and technical simplification.