Unsupervised domain adaptation for in-field cotton boll status identification

Abstract

In-field identification of cotton boll status is an important indicator of maturity grading and precise field management. However, the growth status of cotton boll is highly affected by environmental factors. Differentiation and correlation exhibit in data distribution of different domains, caused by distinct districts, time, weather, and farming operations. Therefore, distribution mismatch is a common phenomenon in agricultural image acquisition such that traditional manual observation measures or standard classification models that are independent and identically distributed (i.i.d.) often cannot obtain satisfactory results. One feasible solution to address this problem is to use domain adaptation that adapts knowledge from the original training data, a.k.a. the source domain, to the new testing data, a.k.a. the target domain. In this paper, we propose a novel NCA-based unsupervised domain adaptation method termed NCADA, which includes three procedures: feature extraction using a deep CNN, feature transformation matrix generation, and target label inference. We validate the NCADA method on our constructed in-field cotton boll dataset with 1,600 images. Extensive experiments show that NCADA method achieves accurate identification performances of 86.4%,85.3% and 81.2% on ‘Internet → Field’ and two different ‘Field → Field’ settings, demonstrating that NCADA can be a useful tool to replace manual observation and standard classification methods.