Greenness identification using visible spectral colour indices for site specific weed management

An aggregated distribution pattern of weed populations provides opportunity to reduce the herbicide application if site-specific weed management is adopted. This work is focused on the practical testing of site-specific weed management in a winter wheat and the optimisation of the control thresholds. Patch spraying was applied to an experimental field in Central Bohemia. Total numbers of 512 application cells were arranged into 16 blocks, which allowed the randomisation of four treatments in four replications. Treatment 1 represented blanket spraying and the other treatments differed by the herbicide application thresholds. The weed infestation was estimated immediately before the post-emergence herbicide application. Treatment maps for every weed group were created based on the weed abundance data and relevant treatment thresholds. The herbicides were applied using a sprayer equipped with boom section control. The herbicide savings were calculated for every treatment and the differences in the grain yield between the treatments were tested using the analysis of variance. The site-specific applications provided herbicide savings ranging from 15.6% to 100% according to the herbicide and application threshold used. The differences in yield between the treatments were not statistically significant (P = 0.81). Thus, the yield was not lowered by site-specific weed management.

Download Full-text

Spatial and Temporal Distribution of Ecballium elaterium in Almond Orchards

Agronomy ◽

10.3390/agronomy9110751 ◽

2019 ◽

Vol 9 (11) ◽

pp. 751 ◽

Cited By ~ 1

Author(s):

Lior Blank ◽

Nitzan Birger ◽

Hanan Eizenberg

Keyword(s):

Spatial Pattern ◽

Weed Management ◽

Nearest Neighbor ◽

Temporal Distribution ◽

Nearest Neighbors ◽

Chemical Application ◽

Site Specific ◽

Herbicide Treatments ◽

Suitable Target ◽

Almond Orchards

The concept of site-specific weed management is based on the assumption that weeds are aggregated in patches. In this study, we surveyed four plots in four commercial almond orchards for three years and mapped the locations of Ecballium elaterium, a troublesome weed in Israeli agriculture, specifically in almond orchards. We analyzed the spatial pattern of the plants’ locations using nearest neighbor analysis and Ripley’s L function. The number of E. elaterium plants increased by more than 70% in the four plots from 2015 to 2016. In addition, the observed mean distance between nearest neighbors increased by more than 10% from 2016 and 2017. We found in all four plots that the spatial pattern of E. elaterium was clustered and that these weed patch locations were consistent over the years although the density within the patches increased. The extent of these clusters ranged between 40 to 70 m and remained similar in size throughout the study. These features make E. elaterium a suitable target for site-specific weed management and for pre-emergence patch spraying. Knowledge of the spatial and temporal pattern of weeds could aid in understanding their ecology and could help target herbicide treatments to specific locations of the field and, thus, reducing the chemical application.

Download Full-text

SITE SPECIFIC WEED MANAGEMENT MAPPING SYSTEM USING UNMANNED AERIAL VEHICLE (UAV)

10.13031/aim.201900207 ◽

2019 ◽

Author(s):

Bruen G Smith ◽

Livia Faria Defeo ◽

Troy A Jensen

Keyword(s):

Unmanned Aerial Vehicle ◽

Weed Management ◽

Site Specific ◽

Mapping System ◽

Aerial Vehicle

Download Full-text

RETRACTED: Advanced techniques for Weed and crop identification for site specific Weed management

Biosystems Engineering ◽

10.1016/j.biosystemseng.2011.02.002 ◽

2011 ◽

Vol 109 (1) ◽

pp. 52-64 ◽

Cited By ~ 14

Author(s):

Karan Singh ◽

K.N. Agrawal ◽

Ganesh C. Bora

Keyword(s):

Weed Management ◽

Site Specific

Download Full-text

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Plants ◽

10.3390/plants9050559 ◽

2020 ◽

Vol 9 (5) ◽

pp. 559

Author(s):

Mojtaba Dadashzadeh ◽

Yousef Abbaspour-Gilandeh ◽

Tarahom Mesri-Gundoshmian ◽

Sajad Sabzi ◽

José Luis Hernández-Hernández ◽

...

Keyword(s):

Weed Management ◽

Rice Field ◽

Vision System ◽

Geometric Mean ◽

Texture Features ◽

Learning System ◽

Metaheuristic Algorithms ◽

Test Set ◽

Site Specific ◽

Mean Values

Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.

Download Full-text