Identifying Cotton Fields from Remote Sensing Images Using Multiple Deep Learning Networks

Haolu Li,Huihui Song,Solomon Obiri Yeboah Amankwah,Guojie Wang,Zhen Dong,Mengjuan Wu,Xikun Wei

doi:10.3390/agronomy11010174

Haolu Li, Huihui Song + Show 5 more

Open Access

https://doi.org/10.3390/agronomy11010174

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Abstract

Remote sensing imageries processed through empirical and deterministic approaches help predict multiple agronomic traits throughout the growing season. Accurate identification of cotton crop from remotely sensed imageries is a significant task in precision agriculture. This study aims to utilize a deep learning-based framework for cotton crop field identification with Gaofen-1 (GF-1) high-resolution (16 m) imageries in Wei-Ku region, China. An optimized model for the pixel-wise multidimensional densely connected convolutional neural network (DenseNet) was used. Four widely-used classic convolutional neural networks (CNNs), including ResNet, VGG, SegNet, and DeepLab v3+, were also used for accuracy assessment. The results infer that DenseNet can identify cotton crop features within a relatively shorter time about 5 h for training convergence. The model performance was examined by multiple indicators (P, F1, R, and mIou) produced through the confusion matrix, and the derived cotton fields were then visualized. The DenseNet model has illustrated considerable improvements in comparison with the preceding mainstream models. The results showed that the retrieval precision was 0.948, F1 score was 0.953, and mIou was 0.911. Furthermore, its performance is relatively better in discriminating cotton crop fields’ fine structures when clouds, mountain shadows, and urban built up.

Highlights

Cotton (Gossypium hirsutum L.) is an important economic crop in China
We introduced precision (P), recall (R), F-measure (Fα), and mean intersection over union to make a quantitative evaluation of different convolutional neural networks (CNNs) networks based on the confusion matrix
We have first made DenseNet pre-training with different types, ensuring that the DenseNet layer is optimal for cotton identification

Summary

Introduction

Cotton (Gossypium hirsutum L.) is an important economic crop in China. Xinjiang is the largest cotton producer in China, occupying an important income source both domestically and internationally. In the same (2018) fiscal year, the cotton production was ~626,316 tons, substantially contributing to the local. Due to an exponential increase in the cotton products demand, the cotton cropped area in Xinjiang has reached up to ~2.5 million hectares during the fiscal year 2019–. 2020 with a 78% production of the national level [2]. The warming trend and changing climate have threatened cotton productivity, especially due to water and energy cycle changes. Recent studies indicated that air humidity changes, precipitation, temperature, and sunshine duration collectively affect biological and cotton stalk productivity [3,4]

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