Abstract
The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.
Highlights
Many agricultural crops require the use of herbicides as essential tools for maintaining the quality and quantity of crop production
We developed an object-based image analysis (OBIA) procedure consisting of three main phases: 1) automatic definition of crop rows within a maize field accomplished by combining spectral and contextual features in a customized looping rule set algorithm, 2) discrimination of weed seedlings and crop plants based on their relative positions, and 3) automatic generation of a weed coverage map in a grid framework adapted to the specification required by the herbicide spraying machinery
1: Weed map information provided by the OBIA procedure
Summary
Many agricultural crops require the use of herbicides as essential tools for maintaining the quality and quantity of crop production. Associated environmental and economic concerns have led to the creation of European legislation on the sustainable use of pesticides [2] This legislation includes guidelines for the reduction in applications and the utilization of adequate doses based on the degree of weed infestation. Both components are integrated in the agronomical basis of the precision agriculture principles and especially of site-specific weed management (SSWM). This consists ofthe application of customized control treatments, mainly herbicides, only where weeds are located within the crop field in order to use herbicides and doses according to weed coverage [3]. Remote sensing technology can play a role here as an efficient and repeatable method to obtain crop field information related to weed infestation
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