A weeding-duration model for Abies sachalinensis plantations in Hok-kaido, northern Japan

I developed a weeding-duration model for Sakhalin fir （Abies sachalinensis （Fr. Schmidt） Masters） plantations that employs a generalized linear model. The number of years following planting that weeding is necessary is the response variable, and elevation, slope steepness, maximum snow depth, annual precipitation, geology, soil, site index, slope aspect, and vegetation type are explanatory variables. Among the explanatory variables, geology, soil, slope aspect, and vegetation type are categorical data. A Poisson distribution is assumed for the response variable, with a log-link function. Elevation, slope steepness, maximum snow depth, annual precipitation, site index, and vegetation type had a significant effect on weeding duration. Among the eight models with the smallest Akaike information criterion （AIC）, I chose the model with no multicollinearity among the explanatory variables. The weeding-duration model includes site index, maximum snow depth, slope steepness （angle） and vegetation type as explanatory variables; elevation and annual precipitation were not included in the selected model because of multicollinearity with maximum snow depth. This model is useful for cost-benefit analyses of afforestation or reforestation with Abies sachalinensis.

The effect of weeding frequency and schedule on weeding operation time: a simulation study on a sugi (Cryptomeria japonica) plantation in Japan

This study quantified the effect of weeding frequency and weeding schedules on weeding operation time in a sugi(Cryptomeria japonica)plantation stand.A weeding operation time estimation model was developed;then the cumulative weeding operation time after six growing seasons was simulated using the developed model.The developed model included weed height,relative height of weeds to sugi,and initial planting density.The simulated cumulative weeding operation time decreased approximately 6%for each one-treatment decrease in weeding frequency.Under a three-treatment weeding frequency scenario,the simulated cumulative operation time when weeding was conducted during non-consecutive years was longer than that when weeding was conducted during three consecutive years.The results suggest that carrying out weeding treatment during consecutive years is the more effective for reduction of weeding costs.We conclude that weeding schedule as well as weeding frequency must be considered for reduction of weeding operation time.

Integrating Mechanical Weeding and Planting for Reduced Labour Input in Paddy Rice under System of Rice Intensification (SRI)

The System of Rice Intensification (SRI) has been attributed to improvement in rice production with various attributes being accrued from application of the SRI Principles. The most notable are savings on water use and increase in yield. Alternate Wetting and Drying (AWD) has also paved way for mechanical weed control in paddy fields. One of the major constraints to adoption of SRI is the perceived increased labour input due to the careful transplanting and frequent weed control. This paper evaluates the effect of mechanization on labour input in SRI in comparison to the less mechanized farmer practice. In attempt to reduce drudgery in transplanting under SRI, the drum seeder was used to establish the rice crop by direct seeding. This was then followed by using SRI practices i.e. AWD and mechanical weeding. Direct seeding using a drum seeder was compared to transplanting in both SRI and the common farmer practice. Hand weeding was also evaluated and compared to mechanical weeding. Labour input cost was also compared to the income accrued from the yields. From the study, it was noted that direct seeding using the drum seeder reduced labour input by 97% compared to transplanting. This was possible in that in direct seeding, and there was no nursery preparation and management as in transplanting. The use of a mechanical weeder reduced labour input by 28.3% in relation to hand weeding. Labour input cost for SRI was cheaper (Kshs. 124,080 per hectare) compared to the common farmer practice (Kshs. 139,117.50 per hectare). There was more yield from the SRI practice (2.75 Ton/ha) compared to the common farmer practice (1.88 Ton/ha).

Influence of Weeding on the Performance of White Maize Varieties

The experiment was conducted at agronomy farm of Sher-e-Bangla Agricultural University, Dhaka from November 2017 to April 2018 to investigate the influence of weeding regimes on the performance of white maize varieties. The experiment comprised two varieties viz. YANGNUO-3000 and PSC-121, designated as V1 and V2 respectively combined with four weed control treatments viz. T0 = No weeding, T1 = One hand weeding at 60 DAS (days after sowing), T2 = two hand weeding at 40 DAS and 60 DAS and T3 = Weed free after 40 DAS. The experiment was laid out in RCBD (factorial) with three replications. PSC-121 showed the superior performance in terms of plant height, leaf number plant-1, number of grains cob-1 (468.75), 100 grains weight (35.0837 g), grain yield (8.28 t ha-1), stover yield (6.56 t ha-1) and harvest index (55.58%) over YANGNUO-3000. In the case of weed control treatments, the highest plant height, leaf number plant-1, number of grains cob-1 (464.54), 100 grains weight (37 g), grain yield (9.25 t ha-1) and stover yield (7.46 t ha-1) were reported from T3. All the parameters studied were found lowest with T0. However, in terms of interaction, no single interaction was superior to other alternatives. But in most of the cases V2T3 showed the highest values regarding the maximum plant height, leaf number plant-1, number of grains cob-1 (494.97) and 100 grains weight (38 g). V2T3 showed the highest grain yield (9.33 t ha-1), whereas, V1T0 showed the lowest grain yield (5.49 t ha-1). The lowest weed density and weed biomass (12.17 no. m-2 and 4.33 g·m-2) was recorded from T3. The highest weed control efficiency (94.38%) was also recorded from T3. In the case of variety V2 showed better performance in terms of weed density, weed biomass and WCE (46.32%).

Chemical Weeding in Dry Direct Seeding Fields of Single Cropping Middlelate Rice

Chemical weeding in dry direct seeding fields of single cropping middle-late rice was studied in Huida vegetable farm of Huizhou City in 2012. The main treatment was herbicide（ pretilachlor ＋ bensulfuron-methyl,Yangguo and butachlor）,and the sub-treatment was application method（ soil treatments,seedling treatment and integrated treatment）. The results showed that 80 g pretilachlor ＋ bensulfuron-methyl（ 36% pretilachlor ＋ 4% bensulfuron-methyl） diluted with 50 kg water could be sprayed or 200 g Yangguo（ 23. 9% butachlor ＋ 1. 1% bensulfuron-methyl） mixed with 15 kg sandy soil could be broadcasted per 667 m2 on the sowing day or the second day under moist condition of soil,which could effectively control weeds in dry direct seeding fields of single cropping middle-late rice.

Impacts of Different Weeding Methods on Weeds Control in Tobacco Fields in Anshun City

[Objective] The paper was to explore weed control measures in tobacco fields in Anshun City. [Method] Different treatments on weed control were conducted in tobacco fields in Anshun City, Guizhou Province, from 2017 to 2019. [Result] Various treatments had no negative effect on tobacco plant growth in the field, and had different degrees of control effects on five dominant weeds, including Digitaria sanguinalis, Setaria viridis, Fagopyrum dibotrys, Commelina communis and Chenopodium album. White mulching film and 50% butralin·clomazone EC 160 m L/667 m^(2) + white mulching film had the worst performance, which had extremely significant or significant differences with other treatments. There was no sig-nificant difference among most treatments, and the overall effects were comprehensive(multi-factor) treatment > double factor treatment > single factor treatment. [Conclusion] Combination control is recommended in practical tobacco production.

Design and test of post-seat weeding machine for paddy

Aiming to solve problems in organic rice weeding a type of post-seat weeding machine was designed for rice paddies.The differences in the strengths and lengths of the root systems between the rice seedlings and the weeds were studied,and the motion track of the weeding wheel was analyzed to obtain the structural parameters of the weeding wheel.Then,the structural model of the weeding wheel was designed.The interaction process between the weeding wheel and soil in the paddy fields was simulated and analyzed based on the discrete element method,so as to investigate the working resistance change tendency of the weeding wheel and the orderliness of the soil disturbances.An orthogonal test was designed in this study,with three factors:the hoeing depth,the rotation rate of the weeding wheel,and the forward speed of the device.The influences of different operation parameters of the weeding machine on the weeding torque and soil disturbance speed were obtained based on a variance analysis of the test results.A multi-index comprehensive weighted scoring method was used to evaluate the simulation results.A soil bin test was conducted to verify the simulation results.Field experiments were carried out to test the working performance of the weeding machine.The comprehensive scoring results indicated that a better working performance of the weeding operation could be obtained when the hoeing depth was 50 mm,the rotation rate of the weeding wheel was 240 r/min,and the forward speed was 0.6 m/s.The results of the soil bin test were consistent with the simulation results.The results of the field experiment revealed that the weeding machine met the requirements for organic rice weeding.These results can provide a reference for the design of weeding machines for paddy fields.

Optimized design and experiment of spiral-type intra-row weeding actuator for maize(Zea mays L.)planting

Mechanical weeding not only avoids crop herbicide residue but also protects the ecological environment.Compared with mechanical inter-row weeding,mechanical intra-row weeding needs to avoid crop plants,which is conducive to causing a higher rate of seedling damage.In order to realize maize(Zea mays L.)intra-row weeding,a maize intra-row weeding mechanism was designed in this study.The mechanism can detect maize seedlings by infrared beam tube,then a sliding-cutting bevel tool moves spirally amid maize seedlings,so as to eradicate intra-row weeds.A field experiment was conducted under the following experimental conditions:the bevel tool rotation speed was 800-1400 r/min,the mechanism forward speed was 4-7 km/h,and the bevel tool depth was 2-14 cm,the experimental results illustrated that the mechanism’s average weeding rate and seedling damage rate were 95.8%and 0.6%,respectively.The variance analysis showed that the primary and secondary factors that affecting the weeding rate and seedling damage rate were the same,which were bevel tool rotation speed,mechanism forward speed,bevel tool depth in soil in a descending order according to the significances.The result of the field experiment may provide a reference for intra-row weeding device design.

Technical Points of Green Prevention and Control Technology of Major Diseases and Pests in Lixian Rhubarb(Rheum palmatum L.)

Based on different types of diseases,pests and weeds in the whole growth period of rhubarb(sowing period-harvesting period),the corresponding green prevention and control technology is proposed,aiming to further reduce the application amount of pesticides and fertilizers in the production of medicinal sources of Lixian rhubarb during the"14 th Five-Year Plan"period.The results will provide a theoretical basis for increasing the promotion and application of agricultural prevention and control(including disease-resistant varieties,ecological regulation),physical prevention and control,biological prevention and control measures,thus ensuring effective protection of the ecological environment,green,healthy and sustainable development of traditional Chinese medicine agriculture in Longnan,and source quality of authentic medicinal materials.

Weeding Out the Corrupt

China takes a tough stand on bribery and corruption of officials The Communist Party of China’s (CPC) Central Commission for Discipline Inspection (CCDI) had a busy year in 2009 as it worked to fight corruption. At CCDI’s annual work review press conference on January 7, Deputy Secretary Gan Yisheng recited a list

Development and Fabrication of Manually Push-Pull Type Conical Weeder for Bangladesh Condition

In Bangladesh, the use of machinery in agriculture production is fast rising. Researchers are developing technology to replace traditional hand weeding to manage weeds in rice fields. The present study has been taken to increase weeding efficiency and reduce the drudgery in weeding and mulching. A line-to-line distance of 20 cm, the operation is push-pull, and field operating condition at 2 - 4 cm standing water (for softening the field) was the designed hypothesis. The weeder consists of a skid/float, float holder, float adjuster, main body frame, rotor, axel, bush, rotor holder, rotor holder adjuster, handle, handle griper, handle holder, handle height adjuster, nut, bolt, etc. The designed weeder was fabricated using MS sheet, MS pipe, MS flat bar, MS nut-bolt, etc. When the rotors perform back and forth, the weeder’s two conical rotors with six plain blades and six serrated blades work together to uproot and bury the weeds. It also contains a 2 mm thick float assembly with a precise angle of 22 degrees. Weeds are uprooted by the weeder’s blades and buried in the muddy soil. It causes topsoil disturbance and enhances aeration. The weeding efficiency and capacity of the conical weeder were 81.92% and 0.0203 ha/h respectively. With a push-pull operation, the weeder can uproot and bury the weeds in a single row at a time. The pushing force and weight of weeder were 43.42 N and 5.6 kg respectively. Farmers can use this weeder to increase their comfort and reduce the drudgery associated with weeding and mulching in their fields.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

Cultivation system influenced the critical period for weed control in cotton field

Background The critical period of weed control(CPWC) refers to the period of time during the crop growth cycle when weeds must be controlled to prevent yield losses.Ultra-narrow row(UNR) is a method of planting of cotton in rows that are 25 cm or less apart.Amongst cultural techniques for weed control,the use of narrow row spacing is considered to be a most promising approach that can effectively suppress weed growth and provide greater yields in cotton.This cultivation system can shorten the length of the critical weed-crop interference duration and results in greater yield.The current research aimed to determination of critical time of weed control in cotton(Gossypium hirsutum L.) under conventional and ultra-narrow row spacing conditions.Field experiments were arranged as factorial experiment in a randomized complete block design with three replications.Factors were cultivation system(conventional(50 cm row spacing) and ultra narrow row(25 cm row spacing and weed treatment including 30,45,60,and 75 days weeding after emergence during the growing season(weed free),and 30,45,60,and 75 without weeding(weed infested) in the growing season along with weedy and weed-free from sowing to harvesting.A four-parameter loglogistic model was fit to the two sets of relating relative crop yield to data obtained from increasing durations of weed interference and lengths of weed-free period.Results In both years and cultivation systems,the relative yield of cotton decreased with the increasing duration of weed-interference but increased with the increasing duration of weed-free period.Ultra-narrow row cultivation delayed the beginning of the CPWC in cotton.Under ultra-narrow row condition,the CPWC ranged from 21 to 99 days after germination in 2021 and 23 to 91 days in 2022 based on the 5% acceptable yield loss.Under conventional cultivation CPWC ranged from 17 to 102 days after emergence in 2021 and 18 to 95 days after emergence in 2022.Conclusions Under both conventional and Ultra-narrow row conditions,weed interference reduces seed yield.Under ultra-narrow row condition,weed interference until 21.1–23.5 days after cotton emergence and under conventional condition,weed interference until 16.9–18.5 days after cotton emergence had not significant reduction on cotton yield.

Effect of Bt traits on transgenic rice's growth and weed competitiveness

Transgene escape could lead to genetically modified rice establishing wild populations in the natural environment and competing for survival space with weeds.However,whether the expression of the Bacillus thuringiensis(Bt)gene in rice will alter the relationship between transgene plants and weeds and induce undesirable environmental consequences are poorly understood.Thus,field experiments were conducted to investigate the weed competitiveness and assess the ecological risk of transgenic Bt rice under herbicide-free and lepidopterous pest-controlled environments.Results showed that weed–rice competition in the direct-sowing(DS)field was earlier and more severe than that in the transplanting(TP)field,which resulted in a significant decrease in biomass and yield in DS.However,conventional Bt and non-Bt rice yield was not significantly different.The weed number,weed coverage ratio,and weed diversity of conventional Bt rice were significantly higher than those of non-Bt rice at the early growth and mature stages,especially in DS plots,suggesting that Bt traits did not increase the weed competitiveness of transgenic rice and had no negative effect on weed diversity.Grain yield and weed number varied between different hybrid rice lines,but those differences were insignificant between Bt and non-Bt rice.The number of insects increased with the increase of weeds in hybrid rice plots,whereas the insect number and diversity did not display a significant difference between Bt and non-Bt rice.Therefore,the ecological risk of transgenic Bt rice is comparable to non-Bt rice.

Evaluation of Pre-Emergence and Post-Emergence Herbicides for Weed Management in Miscanthus sacchariflorus and Miscanthus sinensis

Miscanthus,is a promising bioenergy crop,considered superior to other bioenergy crops because of its higher water and nutrient use efficiency,cold tolerance,and higher production of biomass.Broadleaf weeds and grass weeds,cause major problems in the Miscanthus field.A field experiment was conducted in 2018 and 2019,to assess the effects of pre-emergence(alachlor and napropamide)and post-emergence herbicides(nicosulfuron,dicamba,bentazon,and glufosinate ammonium)on broadleaf and grass weeds in M.sinensis and M.sacchariflorus fields.The weed control efficiency and phytotoxicity of pre-and post-emergence herbicides were evaluated at 30 days after treatment(DAT)and compared to those of the control plots.The results showed wide variations in the susceptibility of the weed species to the treated herbicides.Treatment with nicosulfuron 40 g.a.i.ha^(-1) provided the most effective overall weed control(with 10%visual injury),without affecting the height and biomass of neither Miscanthus species in the field.Post-emergence herbicides such as glufosinate ammonium 400 g.a.i.ha^(-1) and dicamba 482 g.a.i.ha^(-1) were effective and inhibited the growth and density of the majority of weeds to a 100%;however,they showed significant phytotoxicity(toxicity scale of 1-10)to both species of Miscanthus.The application of glufosinate ammonium caused severe injuries to the foliar region(90%visual injury)of both Miscanthus sps.Comparatively,M.sinensis showed a slightly higher tolerance to the herbicides nicosulfuron,bentazon and napropamide with 10%visual injury at the recommended dose than M.sacchariflorus.The present study clearly showed that infestation of broadleaf and grass weeds in Miscanthus fields can cause significant damage to the growth and biomass of Miscanthus and applying pre-emergence and post-emergence herbicides effectively controls the high infestation of these weeds.

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

An Adaptive Edge Detection Algorithm for Weed Image Analysis

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops.Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy.The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields.Some weed methods have been proposed for these fields;however,these algorithms still have challenges as they were implemented against controlled environments.Therefore,in this paper,a weed image analysis approach has been proposed for the system of weed classification.In this system,for preprocessing,a Homomorphic filter is exploited to diminish the environmental factors.While,for feature extraction,an adaptive feature extraction method is proposed that exploited edge detection.The proposed technique estimates the directions of the edges while accounting for non-maximum suppression.This method has several benefits,including its ease of use and ability to extend to other types of features.Typically,low-level details in the formof features are extracted to identify weeds,and additional techniques for detecting cultured weeds are utilized if necessary.In the processing of weed images,certain edges may be verified as a footstep function,and our technique may outperform other operators such as gradient operators.The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification.Finally,the features have been used in logistic regression for weed classification.The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains.The proposed approach attained weighted average recognition of 98.5%against the weed images dataset.Hence,it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments.

Estimating Carbon Capture Potential of Fallow Weeds in Rice Cropping Systems

Weeds occurred during the fallow season can well perform the function of carbon(C)capture due to receiving little human disturbance.This study aimed to evaluate the C capture potential of fallow weeds in rice(Oryza sativa L.)cropping systems.A six-region,two-year on-farm investigation and a three-year tillage experiment were conducted to estimate C capture in fallow weeds in rice cropping systems.The on-farm investigation showed that the average mean C capture by fallow weeds across six regions and two years reached 112 g m^(-2).The tillage experiment indicated that no-tillage practices increased C capture by fallow weeds by 80%on average as compared with conventional tillage.The results of this study not only contribute to an understanding of C capture potential of fallow weeds in rice cropping systems,but also provide a reference for including fallow weeds in the estimation of vegetative C sink.

Intelligent Fish Behavior Classification Using Modified Invasive Weed Optimization with Ensemble Fusion Model

Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management deci-sions about recirculating the aquaculture system while decreasing labor.The classic detection approach involves placing sensors on the skin or body of the fish,which may interfere with typical behavior and welfare.The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy.This study develops an intelligent fish behavior classification using modified invasive weed optimization with an ensemble fusion(IFBC-MIWOEF)model.The presented IFBC-MIWOEF model focuses on identifying the distinct kinds of fish behavior classification.To accomplish this,the IFBC-MIWOEF model designs an ensemble of Deep Learning(DL)based fusion models such as VGG-19,DenseNet,and Effi-cientNet models for fish behavior classification.In addition,the hyperparam-eter tuning of the DL models is carried out using the MIWO algorithm,which is derived from the concepts of oppositional-based learning(OBL)and the IWO algorithm.Finally,the softmax(SM)layer at the end of the DL model categorizes the input into distinct fish behavior classes.The experimental validation of the IFBC-MIWOEF model is tested using fish videos,and the results are examined under distinct aspects.An Extensive comparative study pointed out the improved outcomes of the IFBC-MIWOEF model over recent approaches.

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.