Weed–Crop Segmentation in Drone Images with a Novel Encoder–Decoder Framework Enhanced via Attention Modules

Abstract

The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset.

Intelligent pesticide recommendation system for cocoa plant using computer vision and deep learning techniques

“…The advancement of machine learning technology, particularly, deep learning has paved the way for researchers to focus on the field of agriculture for disease detection of crops and precision farming [9]. Deep learning, utilizing advanced image processing and data analysis techniques, offers promising results by mimicking human brains through artificial neural networks [10][11][12][13][14]. Its implementation in agriculture aims to enhance food security by reducing losses and improving crop production efficiency.…”

An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

“…Yu et al [19] used the Deeply Cascaded Semantic Attention Network (DCSAnet) and the Densely Connected Atrous Convolutional Network (DCA module) as the feature extraction network to test image segmentation of soybean and weed images, and they achieved excellent segmentation results. Khan et al [20] proposed a deep learning framework for encoding and decoding structures. The framework utilizes Dese-inception networks and Atrous spatial pyramid pooling modules to extract multi-scale features and contextual information while using channels and spatial attention units to help recover spatial information.…”