Abstract
Recording growth stage information is an important aspect of precision agriculture, crop breeding and phenotyping. In practice, crop growth stage is still primarily monitored by-eye, which is not only laborious and time-consuming, but also subjective and error-prone. The application of computer vision on digital images offers a high-throughput and non-invasive alternative to manual observations and its use in agriculture and high-throughput phenotyping is increasing. This paper presents an automated method to detect wheat heading and flowering stages, which uses the application of computer vision on digital images. The bag-of-visual-word technique is used to identify the growth stage during heading and flowering within digital images. Scale invariant feature transformation feature extraction technique is used for lower level feature extraction; subsequently, local linear constraint coding and spatial pyramid matching are developed in the mid-level representation stage. At the end, support vector machine classification is used to train and test the data samples. The method outperformed existing algorithms, having yielded 95.24, 97.79, 99.59% at early, medium and late stages of heading, respectively and 85.45% accuracy for flowering detection. The results also illustrate that the proposed method is robust enough to handle complex environmental changes (illumination, occlusion). Although the proposed method is applied only on identifying growth stage in wheat, there is potential for application to other crops and categorization concepts, such as disease classification.
Highlights
An estimated doubling in required crop production is projected by 2,050 in order to meet the demand of the rapid growth human population (Tilman et al, 2011)
The experiment is divided into two sections of identifying ear emergence and flowering stages from the digital images acquired in the field
We proposed an automated observing system using computer vision to determine two key growth stages in wheat: ear emergence and flowering time
Summary
An estimated doubling in required crop production is projected by 2,050 in order to meet the demand of the rapid growth human population (Tilman et al, 2011). An approximate 38% increase over current increases in annual crop production rates is required, and on not much more arable land. Further concerns exist around achieving this target in a changing climate, and achieving it sustainably, whereby reducing agricultural inputs to reduce the environmental degradation caused by our agricultural footprint (Tester and Langridge, 2010). With wheat providing 20% of the worlds calorie and protein intake (Braun et al, 2010), the requirement to increase yield and production is widely recognized. Breeding and precision agriculture, including information-based management of agricultural systems, are fundamental for achieving sustainable increases in wheat productivity and production.
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