Kinds Of Weeds Research Articles

Performance of herbicides for managing weed flora in transplanted aman paddy (Oryza sativa L.)

A field investigation was performed during the rainy seasons of 2018 and 2019 at the Instructional Farm, Bidhan Chandra Krishi Viswavidyalaya, Jaguli, Nadia, West Bengal, India (22056'N and 86048'E, 9.75m above mean sea level) with the aim of determining the comparative effectiveness of different herbicides in controlling various kinds of weeds (grass, sedge and broad-leaf) in the transplanted aman paddy. The experiment was laid out in Randomized Block Design having sixteen treatments with three replication, that includes application of either pre-emergence [butachlor, pretilachlor, pyrazosulfuron ethyl and ready mix (RM) of bensulfuron methyl + pretilachlor at 2 days after transplanting (DAT)] or post-emergence [bispyribac sodium and bispyribac sodium + penoxsulum at 20 DAT] herbicides followed by hand weeding at 40 DAT; application of both pre-emergence and post-emergence herbicides; hand weeding at 20 and 40 DAT and weedy check. Hand weeding at 20 and 40 DAT registered significantly lower weed density, weed dry matter and the highest weed control efficiency. Among the herbicidal treatments, ready-mix formulation of bensulfuron methyl 0.6% + pretilachlor 6% (Londax power 6.6%) performed better in controlling weeds of all categories and recorded higher paddy (3.96 t/ha) and straw yield (4.92 t/ha ) with the maximum weed index which were statistically at par with the hand weeded treatment. Hence, to fetch the effective suppression of weed, application of Londax power 6.6% @ 0.66 kg a.i./ha as pre-emergence (at 2 DAT) with hand weeding at 40 DAT can easily replace additional one hand weeding at 20 DAT.

Roman to early medieval cereal farming in the Rhineland: weeds, tillage, and the spread of the mouldboard plough

ABSTRACT A new model for gauging levels of soil disturbance (i.e. tillage) by analysing arable weed assemblages from archaeological contexts is applied to an extensive Roman-to-early medieval archaeobotanical sequence from the region west of Cologne. It tests the hypothesis that increasing use of the mouldboard plough, especially in a three-field system, would result in increased levels of soil disturbance which would be reflected in the kinds of weeds that grew in arable fields. The results point to clear differences in tillage regimes during the Roman period, providing support for the view that military sites were not provisioned by the same networks that supplied the civilian market. They also reveal generally low disturbance levels for the fifth and sixth centuries, indicating a continuing predominance of ard cultivation in the post-Roman period. The majority of seventh- to eighth-century samples had, however, been grown in ‘high disturbance’ conditions, a pattern that continued through the eighth and ninth centuries. Although use of the mouldboard plough within a fully developed three-field system may not have become widespread until the tenth or eleventh century, our evidence suggests that a plough capable of turning over the soil was in use in the Rhineland at a much earlier date.

Optimized Convolutional Neural Network for Robust Crop/Weed Classification

Precision farming makes extensive use of information technology, which also aids agronomists in their work. Weeds typically grow alongside crops, lowering the production of those crops. Weeds are eliminated with the aid of herbicides. Without knowing what kind of weed it is, the pesticide may also harm the crop. The weeds from the farms must be categorized and identified in order to be controlled. Automatic control of weeds is essential to enlarge crop production and also to avoid rigorous hand weeding as labor scarcity has led to a surge in food manufacturing costs, especially in the developed countries such as India. On the other hand, the advancement of an intelligent, reliable automatic system for weed control in real time is still challenging. This paper intends to introduce a new crop/ weed classification model that includes three main phases like pre-processing, feature extraction and classification. In the first phase, the input image is subjected to pre-processing, which deploys a contrast enhancement process. Subsequent to this, feature extraction takes place, where “the features based on gray-level co-occurrence matrix (GLCM) as well as gray-level run-length matrix (GLRM)” are extracted. Then, these extracted features along with the RGB image (totally five channels) are subjected to classification, where “optimized convolutional neural network” (CNN) is employed. In order to make the classification more accurate, the weight and the activation function of CNN are optimally chosen by a new hybrid model termed as the hybridized whale and sea lion algorithm (HW–SLA) model. Finally, the superiority of the adopted scheme is validated over other conventional models in terms of various measures.

DenseNet weed recognition model combining local variance preprocessing and attention mechanism.

The purpose of this paper is to effectively and accurately identify weed species in crop fields in complex environments. There are many kinds of weeds in the detection area, which are densely distributed. The paper proposes the use of local variance pre-processing method for background segmentation and data enhancement, which effectively removes the complex background and redundant information from the data, and prevents the experiment from overfitting, which can improve the accuracy rate significantly. Then, based on the optimization improvement of DenseNet network, Efficient Channel Attention (ECA) mechanism is introduced after the convolutional layer to increase the weight of important features, strengthen the weed features and suppress the background features. Using the processed images to train the model, the accuracy rate reaches 97.98%, which is a great improvement, and the comprehensive performance is higher than that of DenseNet, VGGNet-16, VGGNet-19, ResNet-50, DANet, DNANet, and U-Net models. The experimental data show that the model and method we designed are well suited to solve the problem of accurate identification of crop and weed species in complex environments, laying a solid technical foundation for the development of intelligent weeding robots.

A green and economical weeds control agent suitable for drone

Weeds are one of the main factors affecting orchard yield, fruit quality and economic benefits. However, present methods have been lower effect and high labor intensity. To inhance the effect of weeds control on orchard, a new green and economic agent suitable for drone with choline chloride and eucalyptus oil as the main components was developed. Adopting optimized “3414” test design, the paper studied the prevention and control effect of this new weed control agent on 8 kinds of main weeds in the orchards of the Yellow-Huai Rivers. The results showed that T5 and T12 were significantly (P≤0.05) control the growth speed of 8 kinds of weeds, T4 was only inhibit the growth of Erigeron canadensis, Digitaria sanguinalis and Artemisia annua, T11 was only inhibit the growth of Tribulus terrestris, Humulus scandens and Cyperus rotundus, and T3 and T13 were promote the growth of weeds, which had the worst control effect among all treatments. Combined with the economic benefit analysis, the cost per 667m2 of weed control of T12 was 124 RMB, which was the most economical compared with manual weeding and mechanical weeding. Therefore, applying 30g choline chloride, 120 mL eucalyptus oil and 75g sucrose ester to each 667m2 is recommended to be popularized as a new green and economical weed control agent in the orchard production of the Yellow-Huai Rivers.

Discovery of (5-(Benzylthio)-4-(3-(trifluoromethyl)phenyl)-4H-1,2,4-triazol-3-yl) Methanols as Potent Phytoene Desaturase Inhibitors through Virtual Screening and Structure Optimization.

Phytoene desaturase (PDS) is not only an important enzyme in the biosynthesis of carotenoids but also a promising target for herbicide discovery. However, in recent years, no expected PDS inhibitors with new scaffolds have been reported. Hence, a solution for developing PDS inhibitors is to search for new compounds with novel chemotypes based on the PDS protein structure. In this work, we integrated structure-based virtual screening, structure-guided optimization, and biological evaluation to discover some PDS inhibitors with novel chemotypes. It is noteworthy that the highly potent compound 1b, 1-(4-chlorophenyl)-2-((5-(hydroxymethyl)-4-(3-(trifluoromethyl)phenyl)-4H-1,2,4-triazol-3-yl)thio)ethan-1-one, exhibited a broader spectrum of post-emergence herbicidal activity at 375-750 g/ha against six kinds of weeds than the commercial PDS inhibitor diflufenican. Surface plasmon resonance (SPR) assay showed that the affinity of our compound 1b (KD = 65.9 μM) to PDS is slightly weaker but at the same level as diflufenican (KD = 38.3 μM). Meanwhile, determination of the phytoene content and PDS mRNA quantification suggested that 1b could induce PDS mRNA reduction and phytoene accumulation. Moreover, 1b also caused the increase of reactive oxygen species (ROS) and the change of ROS-associated enzyme activity in albino leaves. Hence, all these results indicated the feasibility of PDS protein structure-based virtual screen and structure optimization to search for highly potent PDS inhibitors with novel chemotypes for weed control.

Weed Density Extraction Based on Few-Shot Learning Through UAV Remote Sensing RGB and Multispectral Images in Ecological Irrigation Area.

With the development of ecological irrigation area, a higher level of detection and control categories for weeds are currently required. In this article, an improved transfer neural network based on bionic optimization to detect weed density and crop growth is proposed, which used the pre-trained AlexNet network for transfer learning. Because the learning rate of the new addition layer is difficult to tune to the best, the weight and bias learning rate of the newly added fully connected layer is set with particle swarm optimization (PSO) and bat algorithm (BA) to find the optimal combination on the small data set. Data are transported to the convolutional neural network (CNN) by collecting red-green-blue (RGB) and 5-band multispectral images of 3 kinds of weeds and 3 kinds of crops as data sets, through cutting, rotating, and other operations. Finally, 6 kinds of classifications are implemented. At the same time, a self-constructed CNN based on model-agnostic meta-learning (MAML) is proposed in order to realize the learning of neural networks with small sample and high efficiency, and its accuracy is verified in the test set. The neural networks optimized by two bionic optimization algorithms are compared with the self-constructed CNN based on MAML and histogram of oriented gradient + support vector machine (HOG + SVM). The experimental results show that the combination of learning rate through BA is the best, and its accuracy can reach 99.39% for RGB images, 99.53% for multispectral images, and 96.02% for a 6-shot small sample. The purpose of the classification proposed in this article is to calculate the growth of various plants (including weeds and crops) in the farmland. And various plant densities can be accurately calculated through the plant density calculation formula and algorithm proposed in this article, which provides a basis for the application of variable herbicides by experimenting in different farmlands. Finally, an excellent cycle of ecological irrigation district can be promoted.

An Instance Segmentation-Based Method to Obtain the Leaf Age and Plant Centre of Weeds in Complex Field Environments.

Leaf age and plant centre are important phenotypic information of weeds, and accurate identification of them plays an important role in understanding the morphological structure of weeds, guiding precise targeted spraying and reducing the use of herbicides. In this work, a weed segmentation method based on BlendMask is proposed to obtain the phenotypic information of weeds under complex field conditions. This study collected images from different angles (front, side, and top views) of three kinds of weeds (Solanum nigrum, barnyard grass (Echinochloa crus-galli), and Abutilon theophrasti Medicus) in a maize field. Two datasets (with and without data enhancement) and two backbone networks (ResNet50 and ResNet101) were replaced to improve model performance. Finally, seven evaluation indicators are used to evaluate the segmentation results of the model under different angles. The results indicated that data enhancement and ResNet101 as the backbone network could enhance the model performance. The F1 value of the plant centre is 0.9330, and the recognition accuracy of leaf age can reach 0.957. The mIOU value of the top view is 0.642. Therefore, deep learning methods can effectively identify weed leaf age and plant centre, which is of great significance for variable spraying.

Performance yield components of sweet corn (Zea mays Saccharata (Sturt.)) Bailey and weed growth with treatment in different kinds of fertilizer

This study aims to determine the performance yield component of sweet corn and weed density due to different kinds of fertilizer treatments. This study uses a randomized complete block design (RCBD) in factorial patterns with two factors. The first factor is biofertilizer-Arbuscular mycorrhiza fungi (biofertilizer-AMF) which consists of four levels, namely without biofertilizer-AMF (A0), biofertilizer-AMF 5 g/planting hole (A1), biofertilizer-AMF 10 g/planting hole (A2), biofertilizer-AMF 15 g/planting hole (A3). The second factor is cow manure fermented consisting of three levels, namely without cow manure (B0), cow manure 5 tons ha-1 (B1), cow manure 10 tons ha-1 (B2) with 3 replications so that there are 36 experimental units. The variables observed in this study were: cob length (cm), cob diameter (cm), number of seed rows, cob weight with husk (g), cob weight without husk (g) productivity of sweet corn plants (ton ha-1) and kinds of weeds and their absolute density. The results showed that the highest average corn crop productivity was obtained in the treatment without biofertilizer-AMF and cow manure fermented 10 tons ha-1 (A0B2) as 8.52 tons ha-1. If the combination of treatments contained biofertilizer-AMF and cow manure fermented, the highest average productivity of sweet corn was obtained in the treatment of biofertilizer-AMF 5 g/planting hole and cow manure fermented 10 tons ha-1 (A1B2) as 7.19 tons ha-1. The highest weed density from broadleaf is H.capitata (34.167%), from grasses is I.cylindrica (32.432%) and from sedges is Cyperus sp (30.21%).

The Effect of Jussiaea Octovalvis Weed Densities on the Growth and Yield of Several Introduced Vietnam Rice (Oryza Sativa) Varieties

Rice is staple grain production that plays an important role in food security and the socio-economic value of agriculture in South East Asia countries. Among the factors that have negative effects on rice, weed is the major one. Its production constraint is directly seeded rice; besides, weed also affects rice growth and yield by competition about light, water, nutrient, space. Jussiaea octovalvis is a kind of weed in the field during rice growth to mature, which affects the growth and yield of rice but not significantly different based on this research. Treatments were arranged factorially in Randomized Block Design (RCBD) with two factors, 20 treatments (4 varieties, 5 of weed densities), and four replications, and each repetition used two pots for each treatment. As a result, the data showed the difference between the rate of growth and the yield of rice grain recovery products. Especially at data of LL 58 DAP showed significant difference (P=0.017), which the highest rate is 59.13 cm of V2; for harvest data showed that V3 is the best result, but it is also a sensitive one in the condition of weed densities impaction more than the others varieties (D0V3=620.5; D4V3=438). Besides, the measurement of W1000 of rice grain (30.96 g) and Wt per pot (20.01 g) of D0V3 is weight more than the other treatment; within D4V4 was obtained lowest of W1000 (19.26 g) and the lowest of Wt is D1V1 (5.25 g). Besides that, D2 and D4 are more effective on rice growth and yield, and it was non–significant. However, it is not the interaction between weed densities and varieties of rice.

Environmental disappearance of acetochlor and its bioavailability to weed: A general prototype for reduced herbicide application instruction

The consecutive application of herbicide acetochlor has resulted in the widespread drug resistance of weeds and the high risks to environment and human health. To assess environmental behaviors and minimal dosage of acetochlor application in the realistic soil, we systematically investigated the acetochlor adsorption/desorption, mobility, leaching, degradation, weed bioavailability and lethal dosage of acetochlor in three soil types including Nanjing (NJ), Yancheng (YC) and Yingtan (YT). Under the same conditions (60% moisture and darkness), acetochlor had a half-life of disappearance 3 days in NJ, 4.9 days in YC and 25.7 days in YT soils. The HRLC-Q-TOF-MS/MS analyses identified ten metabolites and eight conjugates generated through dealkylation, hydroxylation, thiol conjugation and glycosylation pathways. The acetochlor adsorption to soils ranked in the order of YT > YC > NJ and was committed to the Freundlich model. By examining the effects of soil moisture, microbial activity, illumination/darkness, etc. on acetochlor degradation in soils, we showed that the chemical metabolisms could undergo multiple processes through soil microbial degradation, hydrolysis or photolysis-mediated mechanisms. The longitudinal migration assay revealed that acetochlor leaching ability in the three soils was YT > YC > NJ, which was negatively associated with the order of adsorption behavior. Four kinds of weed were grown in the acetochlor-contaminated NJ soil. The lethal concentrations for the weed plantlets were 0.16–0.3 mg/kg, much lower than the dosage of realistic field application. Overall, our work provided novel insights into the mechanism for acetochlor behaviors in soils, the natural degradation process in the environment, and the lethal concentration to the tested weed plants.

Challenges of weed management in rice for food security in Africa: A review

Abstract Rice is one of the most important crops for food security in Africa. However, there is a large gap between its present demand and supply. Weed competition is basically one of the major constraints in rice systems in Africa, leading to 48–100% yield reduction and sometimes complete crop failure, thereby threatening food security. Weed control methods currently employed to avoid such losses are predominantly hoe-weeding and herbicide application. Hoe weeding is tedious, inefficient, time consuming, associated with high labour demands and often too expensive for the average farmer to afford. Herbicide use on the other hand, does not provide season-long weed control. Moreover, there are not many herbicides that can control different kinds of weeds with one application. Thus, to optimise yield, financial, social and environmental costs and benefits, integrated and ecological weed management approaches are advocated. Future weed research should therefore be focused on delivering information for the implementation of these approaches. This would require improved knowledge of weed biology and ecology, prioritization of problematic weed species, development of competitive rice cultivars and timely weed control. To address the diversity of weed problems in rice systems in Africa, however, research innovations must take full account of farmer’s local conditions using farmers’ participatory approaches. This review suggests that knowledge-based integrated novel approaches must be developed to assist farmers in coping with the challenges of weed management for sustainable rice production.

Allelopathic effects of water extracts from different parts of foxtail millet straw on three kinds of weeds

We examined the allelopathic effect of extracts from different parts of foxtail millet straw with different concentrations (undiluted water extracts, 10-fold, 50-fold, and 100-fold dilution) on three different kinds of malignant weeds (Amaranthus retroflexus, Chenopodium album, and Setaria viridis) by water extract. In this experiment, we measured biological indicators for seed germination period and potted seedling physiological parameters. The results showed that water extract of foxtail millet leaves and stems had significant allelopathic effects on the three species of weeds, with the effects of different concentrations being different. Undiluted water extract inhibited the growth, while dilution (10-fold, 50-fold, 100-fold dilution) promoted the growth. Under the treatment of undiluted water extract of leaf and stem, the germination rate of three weeds decreased 63.9%, 37.3% and 41.7%, respectively, while root length was only 27.8%, 37.8% and 18.4% of the control. The bud length was only 34.5%, 27.7% and 17.6% of the control. The net photosynthetic rate accounted for 66.6%, 89.9% and 88.2% of the control. The transpiration rate accounted for 69.0%, 87.5% and 56.1% of the control, while the synthesis allelopathic index of the three weeds were -0.699, -0.716 and -0.795 by undiluted water extract, respectively. Results implied strong allelopathic inhibition. The allelopathy promoting effect of dilution increased first and then decreased with the increases of dilution folds. Among which, 50-fold dilution had the strongest promoting effect with the germination rate, root length and bud length of the three weeds being significantly different from those of the control, with the synthesis allelopathic index being 0.261, 0.217, and 0.165, respectively. A large number of associated weeds grow in foxtail millet field which is related to the leaching of allelopathic substances in straw.

Identification Spectral Signature of Weed Species in Rice using Spectroradiometer Handheld Sensor

Productions of rice in Malaysia is less compared to other rice producers such as Thailand. One of the reasons is lack of management in weeds. Losses due to weeds must be solved in rice production system, they interfere with the activities involved in the field throughout crop growing period. The main problem in rice field is to detect the presence of weeds among the paddy plant. It is hard to detect from plain sight for weeds in the green field by farmers. This research developed a technique to detect and control the weeds in the rice field. Spectral signature of weeds was collected and stored in the mobile apps for weed management application. The spectral signature of weeds species is unique and has their own characteristics. It can be used as a reference on how to detect weeds in the rice field using remote sensing. In this study, the result outcomes are mainly targeting to help all the paddy farmers in Malaysia to increase their production numbers of paddy. In addition, paddy farmers will be introduced with much advance technique to spot on the weeds that have been infesting in their paddy field and can prepare with what strategy to control on different kind of weeds. Different weed species has different physiological characteristics and it requires different type of strategy to counter the growth in the paddy field. Every weed in the paddy field need to be recognized in order to plan a control strategy. By using only naked eyes of a human and the limited view through the side view of the paddy field contributes to the problem of recognizing type of weeds that infesting the area.

Peculiarities of distribution of unseeded species in crops of perennial grasses, depending on the composition of the grass mixture and the mode of use of the grass

The article investigated the species composition and dynamics of changes in the contamination of agrophytocenoses of perennial grasses, depending on the composition of the grass mixture and the mode of use of the grass stand. In the crops of perennial grasses, 34 weed species belonging to 12 botanical kind were found in the first three years of use of the grass stand. In the structure of weed infestation of perennial grasses, the species of the Astra kind of weeds dominated, which were represented by stanktis annual, dandelion, yarrow, field thistle.

Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery

This study explores the potential of a novel hyperspectral snapshot mosaic camera for weed and maize classification. The image processing, feature engineering and machine learning techniques were discussed when developing an optimal classification model for the three kinds of weeds and maize. A total set of 185 spectral features including reflectance and vegetation index features was constructed. Subsequently, the principal component analysis was used to reduce the redundancy of the constructed features, and the first 5 principal components, explaining over 95% variance ratio, were kept for further analysis. Furthermore, random forests as one of machine learning techniques were built for developing the classifier with three different combinations of features. Accuracy-oriented feature reduction was performed when choosing the optimal number of features for building the classification model. Moreover, hyperparameter tuning was explored for the optimal selection of random forest model. The results showed that the optimal random forest model with 30 important spectral features can achieve a mean correct classification rate of 1.0, 0.789, 0.691 and 0.752 for Zea mays, Convolvulus arvensis, Rumex and Cirsium arvense, respectively. The McNemar test showed an overall better performance of the optimal random forest model at the 0.05 significance level compared to the k-nearest neighbours (KNN) model.

A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms

Precision Agriculture is an area that can benefit from the latest advances in expert systems. One of the goals is to detect and remove weeds intelligently, so that herbicides are only sprayed in areas where weeds exist. By reducing the amount of herbicides used, the risk of contamination of crop and water resources is avoided, which could produce harmful environmental effects. In this paper, a new computer vision based expert system is presented for identifying potato plants and three different kinds of weeds (Secale cereale L., Polygonum aviculare L. and Xanthium strumarium L.) in order to perform site-specific spraying. The videos were recorded from two Marfona potato crops, with a total area of 4 ha located in Kermanshah–Iran (34°20′17.203′′N, 46°19′56.807′′E), taken with a moving platform with a speed of 0.13 m/s, under outdoor lighting conditions. Applying image processing, 3459 objects were extracted and used to train and test the classifiers. 126 color features and 60 texture features were extracted from each object. The main contribution of the proposed approach was the application of two metaheuristic algorithms to optimize the performance of a neural network classifier: first, the cultural algorithm is used to select the five most effective features, in order to improve computational efficiency; then, the harmony search algorithm is applied to find the optimal configuration of the network. This approach has been compared with a statistical method based on linear discriminant analysis. The experimental results show that the proposed expert system achieves an excellent identification accuracy of 98.38%, requiring less than 0.8 s of execution on an average PC.

Ethno-medicinal survey on weed plants in agro-ecosystems: a case study in Jahrom, Iran

Most useful plant-derived drugs have been discovered by follow-up of ethno-medicinal information. Weeds, unwanted and uncultivated plants in agro-ecosystems, generally tend to have high content of bioactive secondary compounds. The main objectives of present research were to gather, analyze, and evaluate the ethno-medicinal information on weed species used by the native people of Jahrom, Iran. All kinds of weeds were repeatedly collected from various agricultural areas through different seasons of 2015. Ethno-medicinal information about these weeds was obtained through direct interviews and open discussions with local informants. Based on the results, 36 weed species belonging to 18 families and 31 genera had traditional medicinal uses. Asteraceae was the most dominant family having highest number of weed species followed by Fabaceae, Brassicaceae, and Polygonaceae. Most of weeds with therapeutic uses had annual life cycle. Leaf was the most widely consumed part of the weeds followed by seed and fruit. The weeds were traditionally used for curing various health problems such as gastrointestinal disorders, body pain, and metabolic disorders. It can be concluded that certain weeds could play a major role in curing the diseases and health improvements in the local people, especially in developing countries. Undervaluation and ongoing destruction of the endemic medicinal weeds in agro-ecosystems may have a negative impact on availability of the unwritten local knowledge about such valuable weeds in the future.

Low-Altitude Remote Sensing Based on Convolutional Neural Network for Weed Classification in Ecological Irrigation Area

With the development of ecological irrigation area at present, it requires higher detection and control of weeds in irrigation area. In this paper, aiming at the ecological irrigation area, a classification method of weeds based on convolutional neural network(CNN) is proposed. By collecting 3 kinds of weeds and 3 kinds of crops as data sets UAV-based, through cutting, gray scale and so on, data is transported to the CNN. Finally, 6 categories of classifications are implemented. The classification results show that the recognition rate of weeds can reach 95.6%. In order to prevent and control specific weeds, a method of detecting single weeds density is also presented in this paper. The accurate monitoring of weeds in irrigation area can be realized through the method proposed in this paper, and there is basis for precise weed control in later stage.

Synthesis and herbicidal activity of 4, 8-DHT and its derivates

The chemosynthesis and potential as green herbicides of 4,8-dihydroxy-1-tetralone (4, 8-DHT) and its derivates were emphatically addressed in this study. Firstly, the synthesis of 4, 8-DHT from commercially available material 1, 5-dihydroxynaphthalene was carried out through a novel route of five reaction steps. Then, its five derivates including 4-benzoyl-8-hydroxy-1-tetralone, 4-(3-hydroxypropoxy)- 8-hydroxy-1- tetralone, 4-(2,3-dihydroxypropoxy)-8-hydroxy-1-tetralone, 4-hydroxy-8-(3-hydroxy propoxy) −1-tetralone and 4-hydroxy-8-(2,3-dihydroxypropoxy)-1- tetralone were prepared by modifying alcoholic and phenolic hydroxyl in C-4 and C-8 position of 4,8-DHT molecular structure. After that, these synthesized compounds were examined for their toxicity against six kinds of weeds (Lolium perenne, Phalaris arundinacea, Elymus dahuricus, Cichorium intybus, Sorghum sudanense, and Trifolium repens) in vitro. In general, high concentration could generally inhibit while low concentration might promote the growth of weeds. Among these compounds, 4-(3-hydroxypropoxy)-8 −hydroxy-1-tetralone and 4-hydroxy-8- (3-hydroxypropoxy) −1-tetralone showed significant phytotoxic activities against the six tested weeds, while 4-hydroxy-8-(2,3-dihydroxypropoxy) −1-tetralone was less toxic. For all the six tested weeds, E. dahuricus appeared the most sensitive to the treatments of 4, 8-DHT compounds. Hence, it has been suggested that variables including compound type and concentration as well as weed species should be seriously considered in order to develop and utilize the group of 4, 8-DHT compounds as herbicide in future.