Improved detection of planting failures by computer vision

Oliveira, Willer Fagundes de; Vieira, Antonio Wilson; Santos, Silvânio Rodrigues dos; Pimenta, Samy; Silva, Lucas Alves da

doi:10.1590/1678-992X-2023-0257

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Agricultural Engineering • Sci. agric. (Piracicaba, Braz.) 81 • 2024 • https://doi.org/10.1590/1678-992X-2023-0257 copy

Improved detection of planting failures by computer vision

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The management of natural and planted forests can be conducted sustainably by implementing techniques that consider the spatial and temporal variability of the plant and soil. In this context, precision silviculture through remote sensing can play a vital role, mainly when using Unmanned Aerial Vehicles (UAVs) equipped with specific sensors. In the present study, an automated computational routine (based on computer vision techniques) was developed and validated to perform forest inventory in commercial Eucalyptus grandis forests, using an orthophoto mosaic obtained with an RGB sensor built-in to a UAV. The developed routine employs computer vision techniques, including template matching to locate plants, Delaunay triangulation to create a mesh and indicate the predominant orientations of the planting rows, and an adaptation of the Hough transform to estimate the analytical parameters of each row. These parameters are refined using linear regression to generate the lines best fitting the input data. Finally, the failure segments on each row are identified by detecting the plants in each row. A simulation of regular point distribution on the segment is then used to identify the planting failure. This process allows the geolocation of each failure point for replanting to be quantified. The routine has significant potential in the forest inventory, allowing the geolocation of failures with an overall accuracy of 0.97 and 0.99, respectively, and a maximum positional error of 0.15 m and 0.20 m, respectively.

hough transform; point Clustering; precision forestry; remote sensing; unmanned aerial vehicles

References	Method or Processing Algorithm	Plataform/Sensor	Dataset (input)	Dataset (output)	Species or Crop
La Rosa et al. (2020)La Rosa LEC, Oliveira DAB, Zortea M, Gemignani BH, Feitosa RQ. 2020. Learning geometric features for improving the automatic detection of citrus plantation rows in UAV images. IEEE Geoscience and Remote Sensing Letters 19: 1-5. https://doi.org/10.1109/LGRS.2020.3024641 https://doi.org/10.1109/LGRS.2020.302464...	Convolutional encoder-decoder network	UAV-RGB	Orthomosaic	Plantation rows	Citrus (orange)
Di Gennaro and Matese (2020)Di Gennaro SF, Matese A. 2020. Evaluation of novel precision viticulture tool for canopy biomass estimation and missing plant detection based on 2.5D and 3D approaches using RGB images acquired by UAV platform. Plant Method 16: 1-12. https://doi.org/10.1186/s13007-020-00632-2 https://doi.org/10.1186/s13007-020-00632...	2.5D-surface and 3D-alphashape methods (Edelsbrunner and Mücke, 1994Edelsbrunner H, Mücke EP. 1994. Three-dimensional alpha shapes. ACM Transactions on Graphics 13: 43-72. https://doi.org/10.1145/174462.156635 https://doi.org/10.1145/174462.156635... )	UAV-RGB	Point clouds	Biomass and missing plants	Vine
Fareed and Rehman (2020)Fareed N, Rehman K. 2020. Integration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model. ISPRS International Journal of Geo-Information 9: 151. https://doi.org/10.3390/ijgi9030151 https://doi.org/10.3390/ijgi9030151...	DSM-based solutions, PCA component and K-means	UAV-RGB	Point clouds	Plantation rows	Eucalyptus
Rabab et al. (2021) Rabab S , Badenhorst P , Chen YP , Daetwyler HD . 2021. A template-free machine vision-based crop row detection algorithm. Precision Agriculture 22: 124-153. https://doi.org/10.1007/s11119-020-09732-4 https://doi.org/10.1007/s11119-020-09732...	Image segmentation with morphological Operations, triangular matrix and pre-defined parameters	Terrestrial and handheld Camera RGB, Public data set (Vidović et al., 2016Vidovic I, Cupec R, Hocenski Ž. 2016. Crop row detection by global energy minimization. Pattern Recognition 55: 68-86. https://doi.org/10.1016/j.patcog.2016.01.013 https://doi.org/10.1016/j.patcog.2016.01... ) and UAV-RGB	Images taken from the top and side views or Orthomosaic	Crop row	Maize, celery, potato, onion, sunflower and soya bean
Chen et al. (2021) Chen P , Ma X , Wang F , Li J . 2021. A new method for crop row detection using unmanned aerial vehicle images. Remote Sensing 13: 3526. https://doi.org/10.3390/rs13173526 https://doi.org/10.3390/rs13173526...	Image segmentation and Least squares fitting-based	UAV-Multispectral	Orthomosaic	Row line	Cotton and Wheat
Biglia et al. (2022) Biglia A , Zaman S , Gay P , Aimonino DR , Comba L . 2022. 3D point cloud density-based segmentation for vine rows detection and localisation. Computers and Electronics in Agriculture 199: 107166. https://doi.org/10.1016/j.compag.2022.107166 https://doi.org/10.1016/j.compag.2022.10...	A 3D point cloud processing algorithm based on the detection of key points and a density-based clustering approach	UAV-Multispectral	Point clouds	Spatial location of each vine row	Vine
Rocha et al. (2023) Rocha BM , Fonseca AU , Pedrini H , Soares F . 2023. Automatic detection and evaluation of sugarcane planting rows in aerial images. Information Processing in Agriculture 10: 400-415. https://doi.org/10.1016/j.inpa.2022.04.003 https://doi.org/10.1016/j.inpa.2022.04.0...	Image classification (decision tree, linear discriminant analysis and K-nearest neighbors), RGB gradient filter, morphological operations and computational geometry models	UAV-RGB	Orthomosaic	Crop rows and gaps	Sugarcane
Ribeiro et al. (2023) Ribeiro JB , Silva RR , Dias JD , Escarpinati MC , Backes AR . 2023. Automated detection of sugarcane crop lines from UAV images using deep learning. Information Processing in Agriculture 10: 400-415. https://doi.org/10.1016/j.inpa.2023.04.001 https://doi.org/10.1016/j.inpa.2023.04.0...	CNN (U-Net, LinkNet and PSPNet) and VGG16 network structure pre-trained with ImageNet	UAV-RGB	Orthomosaic	Segmentation of crop lines	Sugarcane
Liu et al. (2023) Liu M , Su W , Wang X . 2023. Quantitative evaluation of maize emergence using UAV imagery and deep learning. Remote Sensing 15: 1979. https://doi.org/10.3390/rs15081979 https://doi.org/10.3390/rs15081979...	Deep learning (YOLO V3)	UAV-RGB	Orthomosaic	Seedling count, spacing and size	Maize


1: m, n ← shape(I)
2: dr ←√(m ² + n ² )/R
3: dt ← (2δ)/T
4: P ← zeros (R, T)
5: for each (x, y) ∈ I do
6: if I(x, y) > 0 then
7: for each t from 0 to T do
8: θ = (∅ – δ) + t.dt
9: ρ ← xcos(θ) + ysin(θ)
10: r ← ρ/dr
11: P(r, t) ← P(r, t)+1
12: end for
13: end if
14: end for


1: for each (x _i , y _i ) ∈ P do
2: for each (θ _k , ρ _k ) ∈ L do
3: D(i, k) = \|x _i cos(θ _k ) + y _i sin(θ _k ) – ρ _k \|
4: end for
5: k = argmint{D(i, t)}
6: K(i) = k
7: end for

R (m)	TP	FP	TN	FN	Se	Sp	Acc	Re (%)
0.05	1,196	1,895	5,355	1,934	0.382	0.739	0.631	1.25
0.10	2,242	849	5,355	888	0.716	0.863	0.814	1.25
0.15	2,965	126	5,355	165	0.947	0.977	0.966	1.25
0.20	3,044	47	5,355	86	0.973	0.991	0.984	1.25
0.25	3,057	34	5,355	73	0.977	0.994	0.987	1.25
0.30	3,058	33	5,355	72	0.977	0.994	0.988	1.25
0.35	3,063	28	5,355	67	0.979	0.995	0.989	1.25
0.40	3,067	24	5,355	63	0.980	0.996	0.990	1.25
0.45	3,070	21	5,355	60	0.981	0.996	0.990	1.25
0.50	3,070	21	5,355	60	0.981	0.996	0.990	1.25
1.00	3,087	4	5,355	43	0.986	0.999	0.994	1.25

Escola Superior de Agricultura "Luiz de Queiroz" USP/ESALQ - Scientia Agricola, Av. Pádua Dias, 11, 13418-900 Piracicaba SP Brazil, Phone: +55 19 3429-4401 / 3429-4486 - Piracicaba - SP - Brazil
E-mail: scientia@usp.br

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[1] * Corresponding author < willer.oliveira@unimontes.br >