Identification of corn and weeds on the leaf scale using polarization spectroscopy

Wang²,

Computer and Computing Technologies in Agriculture XI

et al. 2019

In order to alleviate the difficulties in collecting indexes for the analysis of farmland weed communities, we implemented a computer vision technology-based method for the identification of farmland weeds at the species level. By using the super-green and maximum interclass difference methods to obtain a green vegetation binary image, we were able to separate weeds from cultivated crops through multiple etching and the removal of small areas. A BP (back propagation) neural network was used for weed recognition, and the morphological characteristics of the weeds and each region were selected following etching to construct the input matrix of the recognition model for training and testing the BP network. After experimenting with the computational vision method for the identification of five weed species, we discovered that the recognition accuracy rate reached 96%. The results showed that the computer vision method could quickly and accurately extract a weed community analysis index, thereby providing a reference for the intelligent analysis of weed communities.

Section: Discussionmentioning

confidence: 99%

Farmland Weed Species Identification Based on Computer Vision

Wang²,

Computer and Computing Technologies in Agriculture XI

et al. 2019

Comp Rev Food Sci Food Safe

“…Polarization spectroscopy (PS) is a nonlinear pump–probe method that uses a shift in the polarization of the probe beam caused by anisotropic Zeeman state populations produced in the target molecule by the pump beam. The structure and dynamics of the molecule influence the PS signal's line intensity and form (Fenfang et al., 2016). The details of polarization spectroscopy for pesticide detection in food are summarized in Table 5.…”

Section: Ndt Techniquesmentioning

confidence: 99%

“…Furthermore, various substances have varied polarization characteristics, which can compensate for the lack of traditional distant sensing and enhance target object radiation information. Different plants have different polarized reflection qualities, which are influenced by waxy coatings, surface texture, surface roughness, moisture content, and physicochemical properties (Fenfang et al, 2016). The polarization spectroscopic data acquisition device consists of a spectrometer, a light source, a micro galvanometer, and a computer (Figure 6).…”

Section: Polarization Spectroscopymentioning

confidence: 99%

Nondestructive testing methods for pesticide residue in food commodities: A review

Sindhu

Manickavasagan

2023

Pesticides play an important role in increasing the overall yield and productivity of agricultural foods by controlling pests, insects, and numerous plant‐related diseases. However, the overuse of pesticides has resulted in pesticide contamination of food products and water bodies, as well as disruption of ecological and environmental systems. Global health authorities have set limits for pesticide residues in individual food products to ensure the availability of safe foods in the supply system and to assist farmers in developing the best agronomic practices for crop production. Therefore, the use of nondestructive testing (NDT) methods for pesticide residue detection is gaining interest in the food supply chain. The NDT techniques have several advantages, such as simultaneous measurement of chemical and physical characteristics of food without destroying the product. Although numerous studies have been conducted on NDT for pesticide residue in agro‐food products, there are still challenges in real‐time implementation. Further study on NDT methods is needed to establish their potential for supplementing existing methods, identifying mixed pesticides, and performing volumetric quantification (not surface accumulation alone).

“…There are multiple platforms available for data gathering for weed detection in crops, such as digital cameras [37], hand-held spectroradiometers [38], polarization spectroscopy [39], and satellites [40]. However, unmanned aerial vehicles (UAV) are the most popular platforms researchers use to identify weeds in crops, due to their availability, high-quality data delivery, and ease of handling [41].…”

Section: Image Data Collectionmentioning

confidence: 99%

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

et al. 2021

This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.