Intra-row Weed Control Research Articles

Real-time lettuce-weed localization and weed severity classification based on lightweight YOLO convolutional neural networks for intelligent intra-row weed control

Competition for nutrients between intra-row weeds and cultivated vegetables is a major contributor to reduced crop yields. Compared with manual weeding, intelligent robots can improve the efficiency of weeding operations. Developing real-time and reliable robotic systems for weed control in vegetable fields is a significant challenge due to the complexity of real-time identification, localization, and classification of vegetables as well as various weed species. The main purpose of this study was to propose a high-performance, lightweight deep learning model and an intra-row weed severity classification algorithm for real-time lettuce identification and weed severity classification. In this study, a scaling factor (τ = 0.5) was chosen to lightweight the YOLOv7 model. A new Multimodule-YOLOv7-L lightweight model was then developed by combining ECA and CA attention mechanisms, ELAN-B3 and DownC modules. The overall performance of the Multimodule-YOLOv7- L was better than that of other deep learning models, including YOLOv7, YOLOv7-Tiny, YOLOv8m, YOLOv5n-Cabbage, SE: YOLOv5x, YOLOv5s_Ghb, MST-YOLO_CBAM, Citrus-YOLOv7, Pineapple-YOLOv7, MS-YOLOv7 and CBAM-YOLOv7. The precision, recall, mAP@0.5, F1-score, model weight and FPS of the Multimodule-YOLOv7- L model were 97.5 %, 95.7 %, 97.1 %, 96.6 %, 18.4 MB and 37.3 FPS (Image resolution about 3000 × 3000), respectively. An intra-row weed severity classification algorithm based on the Multimodule-YOLOv7-L model was proposed for use in a new mechanical-laser collaborative intra-row weeding robot. The developed algorithm achieved a classification accuracy of 100 % in eight lettuce weed scenarios, with the processing time of a single image ranging from 4-13 ms. The results of this study provided valuable reference for the development of intelligent robots for intra-row weed control. The algorithm proposed in this article can be obtained at https://github.com/H777R/The-intra-row-weed-severity-classification-algorithm.git.

Mechanical Inter- and Intra-Row Weed Control for Small-Scale Vegetable Producers

Small-scale vegetable producers often do not have modern mechanical equipment; as a result, a significant amount of inter-row and all intra-row weeding is performed manually. The development of small, affordable machines increases the competitiveness of organic vegetable production, improves sustainable land use, and reduces dependence on unwanted herbicides. In this study, a simple modular lightweight e-hoe with the capability for both inter-row (1st degree of freedom) and intra-row (2nd degree of freedom) weeding was proposed. The e-hoe uses battery-powered in-wheel drives to move the platform (3rd degree of freedom) and additional drives to operate the tools. The e-hoe was evaluated in a small greenhouse using three different tools: a traditional hoe, an adjusted rounded hoe, and an adjusted spring tine narrow hoe. The experiments were conducted at four different tool rotation speeds, using specially designed 3D-printed models for crops and weeds for evaluation. The results indicate that the efficiency of the e-hoe rates up to 95% when the right tool design and rotation speed are combined. Based on the battery capacity, the machine can be operated for approximately 3.7 h, enabling the weeding of about 3050 plants.

A SPH-YOLOv5x-Based Automatic System for Intra-Row Weed Control in Lettuce

Weeds have a serious impact on lettuce cultivation. Weeding is an efficient way to increase lettuce yields. Due to the increasing costs of labor and the harm of herbicides to the environment, there is an increasing need to develop a mechanical weeding robot to remove weeds. Accurate weed recognition and crop localization are prerequisites for automatic weeding in precision agriculture. In this study, an intra-row weeding system is developed based on a vision system and open/close weeding knives. This vision system combines the improved you only look once v5 (YOLOv5) identification model and the lettuce–weed localization method. Compared with models including YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5n, and YOLOv5x, the optimized SPH-YOLOv5x model exhibited the best performance in identifying, with precision, recall, F1-score, and mean average precision (mAP) value of 95%, 93.32%, 94.1% and 96%, respectively. The proposed weed control system successfully removed the intra-row weeds with 80.25% accuracy at 3.28 km/h. This study demonstrates the robustness and efficacy of the automatic system for intra-row weed control in lettuce.

Intelligent intra-row robotic weeding system combining deep learning technology with a targeted weeding mode

Mechanical weed control is the most promising weed control method for weed management in organic agriculture. Among the various available methods, inter-row weeding technology is relatively mature, and researchers have focused on intra-row weeding technology. The objective of this study was to develop a new intelligent intra-row mechanical robotic weeding system based on deep learning for crop and weed detection. The system consists of a mobile robot platform and two intelligent weeding units. The mobile robot platform provides power support as well as operating conditions for the weeding units. A targeted weeding pattern was proposed based on the deep learning detection results, and protected and targeted weeding zones were established by strict criteria to reduce crop injury rates. In addition, three weed control knives were designed according to different field environments. Field trials showed that among the three weed knives, the plough-surface weeding knife was the most effective and most suitable knife for flat cultivation, and the wedge weeding knife was most suitable for ridge planting. Based on the obtained results, the best weeding method was determined, and the experiment was repeated using a wedge weeding knife under ridge planting conditions. The final weed removal rate was 85.91%, and the crop injury rate was 1.17%. The results demonstrated the feasibility of the proposed intra-row weed control method.

Development of a Three-Dimensional Plant Localization Technique for Automatic Differentiation of Soybean from Intra-Row Weeds

Soybean is a legume that is grown worldwide for its edible bean. Intra-row weeds greatly hinder the normal growth of soybeans. The continuous emergence of herbicide-resistant weeds and the increasing labor costs of weed control are affecting the profitability of growers. The existing cultivation technology cannot control the weeds in the crop row which are highly competitive with the soybean in early growth stages. There is an urgent need to develop an automated weeding technology for intra-row weed control. The prerequisite for performing weeding operations is to accurately determine the plant location in the field. The purpose of this study is to develop a plant localization technique based on systemic crop signalling to automatically detect the appearance of soybean. Rhodamine B (Rh-B) is a signalling compound with a unique fluorescent appearance. Different concentrations of Rh-B were applied to soybean based on seed treatment for various durations prior to planting. The potential impact of Rh-B on seedling growth in the outdoor environment was evaluated. Both 60 and 120 ppm of Rh-B were safe for soybean plants. Higher doses of Rh-B resulted in greater absorption. A three-dimensional plant localization algorithm was developed by analyzing the fluorescence images of multiple views of plants. The soybean location was successfully determined with the accuracy of 97%. The Rh-B in soybean plants successfully created a machine-sensible signal that can be used to enhance weed/crop differentiation, which is helpful for performing automatic weeding tasks in weeders.

Sensor-Based Intrarow Mechanical Weed Control in Sugar Beets with Motorized Finger Weeders

The need for herbicide usage reduction and the increased interest in mechanical weed control has prompted greater attention to the development of agricultural robots for autonomous weeding in the past years. This also requires the development of suitable mechanical weeding tools. Therefore, we devised a new weeding tool for agricultural robots to perform intrarow mechanical weed control in sugar beets. A conventional finger weeder was modified and equipped with an electric motor. This allowed the rotational movement of the finger weeders independent of the forward travel speed of the tool carrier. The new tool was tested in combination with a bi-spectral camera in a two-year field trial. The camera was used to identify crop plants in the intrarow area. A controller regulated the speed of the motorized finger weeders, realizing two different setups. At the location of a sugar beet plant, the rotational speed was equal to the driving speed of the tractor. Between two sugar beet plants, the rotational speed was either increased by 40% or decreased by 40%. The intrarow weed control efficacy of this new system ranged from 87 to 91% in 2017 and from 91 to 94% in 2018. The sugar beet yields were not adversely affected by the mechanical treatments compared to the conventional herbicide application. The motorized finger weeders present an effective system for selective intrarow mechanical weeding. Certainly, mechanical weeding involves the risk of high weed infestations if the treatments are not applied properly and in a timely manner regardless of whether sensor technology is used or not. However, due to the increasing herbicide resistances and the continuing bans on herbicides, mechanical weeding strategies must be investigated further. The mechanical weeding system of the present study can contribute to the reduction of herbicide use in sugar beets and other wide row crops.

Camera-guided Weed Hoeing in Winter Cereals with Narrow Row Distance

Farmers are facing severe problems with weed competition in cereal crops. Grass-weeds and perennial weed species became more abundant in Europe mainly due to high percentages of cereal crops in cropping systems and reduced tillage practices combined with continuous applications of herbicides with the same mode of action. Several weed populations have evolved resistance to herbicides. Precision weed hoeing may help to overcome these problems. So far, weed hoeing in cereals was restricted to cropping practices with row distances of more than 200 mm. Hoeing in cereals with conventional row distances of 125–170 mm requires the development of automatic steering systems. The objective of this project was to develop a new automatic guidance system for inter-row hoeing using camera-based row detection and automatic side-shift control. Six field studies were conducted in winter wheat to investigate accuracy, weed control efficacy and crop yields of this new hoeing technology.A three-meter prototype and a 6-meter segmented hoe were built and tested at three different speeds in 150 mm seeded winter wheat. The maximum lateral offset from the row center was 22.53 mm for the 3 m wide hoe and 18.42 mm for the 6 m wide hoe. Camera-guided hoeing resulted in 72–96% inter-row and 21–91% intra-row weed control efficacy (WCE). Weed control was 7–15% higher at 8 km h−1 compared to 4 km h−1. WCE could be increased by 14–22% when hoeing was combined with weed harrowing. Grain yields after camera-guided hoeing at 8 km h−1 were 15–76% higher than the untreated control plots and amounted the same level as the weed-free herbicide plots. The study characterizes camera-guided hoeing in cereals as a robust and effective method of weed control.

EXPERIMENT STUDY OF AUTONOMOUS SYSTEM FOR INTRA-ROW WEED CONTROL

The study is used to propose and test by experiment a procedure for autonomous hoeing system for intra-row weed control. The proposed system utilizes the RTK-GPS navigation system. The system consists of autonomous vehicle equipped with side-shifting frame and cycloid hoe. The navigation of the system is controlled using a pre-specified plan and implemented in the system internal computer. The internal computer is also attached to a field station using the wireless local area network (WLAN). The performance of the system was measured through an experiment consisted of making rows having small plants and soil conditions similar to the actual field. The results based on chi-square shows that transverse deviation had normal distribution indicating the performance of the side-shift control. The results related to the longitudinal deviation distance between plants and the nearest line trajectories showed good chi-square fit which is an indication of performance of the cycloid hoe control. The result shows that the system is promising and can be used at larger level with suitable adjustments

Effects of seed bed types and weed control methods on the vegetative parameters of long cayenne pepper (Capsicum frutesens L)

Effects of seed bed types and weed control methods on the vegetative parameters of long cayenne pepper (Capsicum frutesens L)

Machines for non-chemical intra-row weed control in narrow and wide-row crops: a review

Intra-row weed control in organic or low-input cropping systems is more difficult than in conventional agriculture. The various mechanical and thermal devices available for intra-row weed control are reported in this review. Low-tech mechanical devices such as cultivators, finger-weeders, brush weeders, and torsionweeders tend to be used in low density crops, while spring-tine harrows are mainly applied in narrow-row high-density crops. Flame weeding can be used for both narrow and wide-row sown crops, provided that the crop is heat-tolerant. Robotic weeders are the most recent addition to agricultural engineering, and only a few are available on the market. Nowadays, robotic weeders are not yet used in small and medium sized farms. In Europe, highincome niche crops are often cultivated in small farms and farmers cannot invest in high-tech solutions. Irrespectively of the choice of low- or high-tech machines, there are several weeders that can be used to reduce the use of herbicides, making of them a judicious use, or decide to avoid them.

Cross-Flaming Application for Intra-Row Weed Control in Maize

Abstract. Flame weeding is the most common thermal intra-row weed control method used in agriculture as an alternative to herbicides in heat-tolerant crops. Within the seventh framework program project “Robot fleets for Highly Effective Agriculture and Forestry Management” (RHEA), the University of Pisa was responsible for the development of an automatic machine for intra-row cross-flaming in maize ( L.). This study focused on the selection of a range of liquid petroleum gas (LPG) doses able to control weeds without affecting crop yields, for the basic calibration of the machine. Tests were conducted in 2012 and 2013 during the growing cycle of maize both in weed-free and real-field weedy conditions. Five biological LPG doses (0, 52, 65, 104, and 130 kg ha-1) were applied at different maize growth stages once (2- and 5-leaf) and twice (2-leaf the first time and 16 days after the first time). The response of maize and weeds to cross-flaming was evaluated in terms of grain yield, weed density after flame weeding, and weed dry biomass at harvest. Log-logistic models were used to describe the responses of different growth stages of maize and weeds to single and repeated applications of LPG doses. Overall response of maize yield to flame weeding was influenced by LPG dose, number of flame weedings, maize growth stage, and presence of weeds. The results of this study indicate that two cross-flaming treatments applied separately with an LPG dose ranging from 36 to 42 kg ha-1 can provide an acceptable level of weed control in maize, enough to ensure economically acceptable yields.

Mechanical weed control on small-size dry bean and its response to cross-flaming

Dry bean (Phaseolus vulgaris L.) can be a profitable crop for farmers; however controlling weeds effectively without a decrease in yield remains a problem. An example where mechanical weed control is difficult to conduct is dry bean ‘Toscanello’, which is a small sized high-income niche product growing low to the ground. Concerning intra-row weed control, also flame weeding could be an opportunity but the dry bean heat tolerance needs to be studied. The aims of this research were to study the weed control efficacy of a spring-tine harrow and an inter-row cultivator in this bean variety, and to test the tolerance of dry bean cultivated under weed-free conditions to cross-flaming applied with different liquefied petroleum gas (LPG) doses. Flame weeding was applied at BBCH 13 and BBCH 14 bean growth stages by pairs of burners producing direct double flame acting into the intra-row space, with bean plants placed in the middle. The results suggest that the spring-tine harrow used two times at BBCH 13 and 14, respectively, lead to a yield similar to that of the weedy control. The inter-row cultivator could be an opportunity for small-sized dry bean crops producers, enabling them to obtain a similar yield compared to the hand-weeded control. Concerning the bean tolerance to cross-flaming the results showed that bean flamed at BBCH 13 stage had little tolerance to cross-flaming. Bean flamed at BBCH 14 stage was tolerant until an LPG dose of 39 kg/ha, giving yield responses similar to those observed in the non-flamed control.

Design and Evaluation of a Levelling System for a Weeding Robot

Abstract: Weed control is a critically important task in organic crop farming. Even though there are machines available for inter-row weeding, manual weeding is still the only choice for weed control in organic farms, especially in the narrow spaces between crop plants (intra-row weeds). Such an operation is highly labor intensive and costly in organic vegetable production. Automatic or robotic weeding could provide a potential solution for addressing labor related issues. In intra-row weed control, weeding end-effectors need to be positioned accurately to remove weeds growing very close to the plant while the robotic vehicle is continuously moving on a generally uneven and uncertain field surface. This study was aimed at assessing the performance of an end-effector auto-levelling system designed to accurately control the position of the end-effector during weeding operations in vegetable crops. The performance assessment was conducted via a set of laboratory experiments using a specifically designed and fabricated proof-of-concept prototype. To achieve the desired level of performance in actual field conditions, the prototype system required maintaining the end-effector base at horizontal position within a ± 0.25o angular error when the testbed (laboratory prototype) roll and pitch angles were varied from -8o to 8o. The test results verified that the developed end-effector base levelling system could maintain the drift of the end-effector tip position within 18 mm when input roll or pitch angle reached 8o. Meanwhile, the corresponding position error caused by angular error of the levelling plate was limited in 0.2 mm when the levelling plate at a height of 10 cm, which means the levelling system can efficiently reduce the effect of the rough field. The regularity of the end-effector tip position drift can also help us with end-effector control.

Innovative Strategies and Machines for Physical Weed Control in Organic and Integrated Vegetable Crops

Weed control is one of the most serious problems in vegetable crops, limiting cultivated plants correct development, yields, product quality and farmers income. Therefore, the aim of this work was to set up and improve innovative strategies and machines for physical (mechanical and thermal) weed control in organic or “integrated” vegetables production in many important areas of Northern, Central and Southern Italy. Therefore, on-farm experiments were carried out since 1999 on fresh marketable spinach, processing and fresh market tomato, cauliflower, savoy cabbage, greenhouse cultivated leaf beet, garlic, chicory, fennel and carrot. These research activities started are still ongoing. The traditional farm weed management system was always compared to one or more innovative strategies that were defined according to the characteristics of the environment (i.e. soil type and conditions, water availability, etc.), typology of cultivation, crop rotation, expected technical and economical results. The innovative strategies were the combination among preventive methods (false or stale seed-bed technique), cultural methods (i.e. crop spatial arrangement that was often adjusted in order to improve operative machines effectiveness) and direct control methods (flaming, precision hoeing, etc.). Different kinds of specific implement such as flex tine and rolling harrows (patented by the University of Pisa, patent n. PI/2004/A/000071), and flamers (designed and realized by the University of Pisa) were used to perform false or stale seed-bed technique. Precision hoes equipped with rigid tools and hoeconformed rolling harrows, equipped with elastic tines for selective intra-row weed control, were used to perform post emergence interventions. The use of the innovative weed management systems always resulted in significant weed abundance reductions (from 70 to 100 %), relevant yield increases, high contractions of manpower requirement (from 20 to 80 %) and consequent relevant reductions of costs and increases of farmers gross incomes (from 15 to 75 %) in comparison with those obtained performing the standard systems. The results of these on-farm experiments emphasise that physical weed control can be effectively performed using the innovative machines designed and built at the University of Pisa. These machines can also be easily adjusted in order to be used in other crops and agricultural contexts. Moreover, the present versions of the machines, realized as “low-tech” implement in order to be available on the market at low costs, were recently modified within the RHEA Project, a 7th Framework Programme EU funded research project, in which an automatic and robotized hoeing-flaming machine able to perform VRA cross flaming was designed, fully realized and tested obtaining very promising results.

Intelligent versus non-intelligent mechanical intra-row weed control in transplanted onion and cabbage

Non-chemical weed control methods are increasingly in demand for conventional field vegetables, mainly accentuated by regulatory restrictions on the use of herbicides. Intelligent camera-based systems capable of guiding mechanical weeding devices so as to avoid injuring crop plants are now available for practise. The aim of the present study was to compare the weeding performance of an intelligent mechanical weeding machine with non-intelligent tools such as a weed harrow, finger and torsion weeders. Two experiments were conducted in transplanted onion and two in transplanted white cabbage, focussing on weed and crop effects. In broad terms, intelligent weeding was not superior to the simpler tools in onion, and only minor differences were found that could be attributed to the settings of the non-intelligent implements. It was only possible to estimate the effects on weeds in the one cabbage experiment with intelligent weeding, which showed no noteworthy differences between the intelligent tool and the simpler ones. Finally, a competition study was conducted on the importance of untreated weeds in close proximity to the transplant as a result of intelligent weeding. It showed that intelligent weeding capable of cultivating the soil close to cabbage plants can provide satisfactory weed control without any need for subsequent manual weeding.

Co-robotic intra-row weed control system

The automation of intra-row weed control in row crop production systems is very challenging. This work describes the development and in-field assessment of an automatic intra-row, hoe-based weeding co-robot system with real-time pneumatic hoe actuation based on an accurate odometry sensing technique. The US National Science Foundation has identified a need for robots (called co-robots) that serve as co-workers and work beside, or cooperatively with, people. These co-robots have a symbiotic relationship with a human partner, where, as a team, they combine their relative strengths to jointly perform a task. Such co-robots should be relatively inexpensive and easy to use. In this work, mechanical weed control was achieved by a co-robot actuator that automatically positioned a pair of miniature hoes into the intra-row zone between crop plants. The design was tested in a precision transplanted row crop and may also be suitable for direct seeded row crops. Co-robot cost was minimised by limiting the system to a single, simple odometry sensor. Co-robot hoe actuation was controlled using pre-programmed knowledge of the crop planting pattern and real-time odometry data as the control input for hoe positioning. Low-frequency drift in the odometry control points relative to the actual plant locations was corrected occasionally as needed in real-time by a human partner monitoring system performance. The co-robot was evaluated in an experimental trial conducted on the UC Davis campus farm. Assessment was based upon the follow-up hand hoeing required after automated intra-row weeding in comparison to the labour required to manually hoe a control plot. The mean person hours required for hand hoeing weeds in the control were 0.241 h for the 100 m2 plot, while only 0.102 h 100 m−2 were required in follow-up labour to complete the weed removal in the plots weeded by the co-robot. This represents a 57.5% reduction in hand labour requirements for intra-row weed control and indicates that the co-robot could help reduce traditional hand hoeing labour requirements with mechanised weed control in intra-row areas between the crop plants.

Field sprayer for inter and intra-row weed control: performance and labor savings

Studies of new tools and methods for weed control have been motivated by increased consumer demand for organic produce, consumer and regulatory demands for a reduction in environmentally harmful herbicide use, and the decreased availability of farm workers willing to perform manual tasks, such as hand weeding. This study describes the performance of a new sprayer system for commercial production that integrates two herbicide applications in a single pass, selective herbicide (SH) application in narrow bands over the crop row, and a non-selective herbicide (NSH) application between crop rows. A real-time kinematic (RTK) global positioning system (GPS) was used for auto-guidance in seeding and spraying operations. Conventional broadcast SHs and experimental treatments were applied at a constant nominal speed of 5.5 km h-1 for comparison. Trials in commercial sugar beet fields demonstrated the following: (i) average hand-weeding time can be reduced by 53% (ii) the new sprayer system reduced SH use by 76%, and (iii) sugar beet density did not change significantly during treatment. These results demonstrate the feasibility of using the new RTK-GPS controller sprayer system for differential and efficient herbicide application in inter- and intra-row zones in row crop production.

Automated weed control in organic row crops using hyperspectral species identification and thermal micro-dosing

A hyperspectral imaging system was coupled to a precision, pulsed-jet, micro-dosing system to selectively deliver high-temperature, organic, food-grade oil for intra-row weed control in early growth tomatoes. The imaging system, based upon a multispectral Bayesian classifier, successfully discriminated the species of 95.9%, on average, of plant canopy for tomato, Solanum nigrum L. and Amaranthus retroflexus L. using canopy reflectance in the 384–810 nm range. Food-grade oil, heated to approximately 160 °C, was applied to weeds using a pressurized micro-dosing pulsed-jet. The target application rate was approximately 0.85 mg/cm2 in 10-ms-pulsed doses while traveling at a ground speed of 0.04 m/s. In an outdoor test, approximately 95.8% of S. nigrum and 93.8% of A. retroflexus were controlled 15 days post thermal treatment, while only 2.4% of the tomato plants received significant damage. Application coverage assessments of leaf surfaces immediately after the heated oil application found that tomato viability was retained if 50% or less of the leaf surface was inadvertently dosed, while 100% mortality was achieved for S. nigrum and A. retroflexus if more than 90% of their respective leaf surfaces were covered with heated oil.

Automated intelligent rotor tine cultivation and punch planting to improve the selectivity of mechanical intra‐row weed control

Rasmussen J, Griepentrog HW, Nielsen J & Henriksen CB (2012). Automated intelligent rotor tine cultivation and punch planting to improve the selectivity of mechanical intra‐row weed control. Weed Research52, 327–337.SummaryThere is much research on technical aspects related to sensor and mapping techniques, which enable so‐called intelligent cultivators to target the intra‐row spaces within crop rows. This study investigates (i) an expected advantage of an intelligent rotor tine cultivator (the cycloid hoe) in terms of crop‐weed selectivity and (ii) an expected synergistic effect between punch planting and post‐emergence weed harrowing, in terms of improved crop‐weed selectivity. Selectivity is defined as the relationship between weed density decline and associated crop density decline 1 week after cultivation. Punch planting is a sowing technique where holes are created in the ground with a minimum of soil disturbance, and seeds are inserted into them, without soil disturbance outside the holes. Two experiments were carried out with the cycloid hoe in organic sugar beet. The rotation tines were guided by RTK‐GPS relative to geo‐referenced sugar beets. Tines were moved into the row when there was enough space between crop plants to cultivate and kept outside when they were predicted to strike a crop plant. The selectivity of the cycloid hoe was tested against two machine variants without intelligent guidance: the rotor tine cultivator in a locked mode, where tines rotate within the crop row without taking crop plants into consideration, and an ordinary flex tine weed harrow. The experiments showed no differences between the three machine variants in terms of selectivity. Five experiments with punch planting in sugar beet and carrot crops showed no synergistic effects between plant establishment procedures and selectivity of post‐emergence weed harrowing. Even if punch planting and automated intelligent rotor tine cultivation were not combined, the results indicated that there was no reason to believe that a combination contributes significantly in mechanical intra‐row weed control in direct sown crops to the solution of the main problem of low selectivity, which still remains a major challenge. Future studies on precision intra‐row cultivation should focus on cutting implements, instead of tine implements that mainly work through soil burial.

Design and testing of an intra-row mechanical weeding machine for corn

As an alternative to chemical weed control, mechanical weed control between crop rows can be achieved using standard tools such as field cultivators. This paper addresses the related problem of achieving mechanical intra-row weed control in maize. The object was to non-specifically remove weed plants within the row by enabling dual tine carriers to engage the soil whilst circumventing the maize stalks. The maize stalks were distinguished from the weeds and maize leaves by utilising 1) the typical vertical quasi-cylindrical stalk of the maize plant, 2) the limited range of maize stalk diameters, and 3) by assuming constant plant spacing. To assess the performance of the machine, a video was taken during field plot experiments. This allowed determination of the number of plants that were “fatally damaged” after inadvertently being pushed over by the implement. This was assumed to cause the plant to die, or “minimally damaged” where the implement merely touched the plant, when the plant was assumed to survive. Experiments were carried out using three arrangements being 1) three rows without weeds, 2) three rows with broadleaf weeds (simulated by planting soybean) and 3) three rows with grassy weeds. The percentage of plants that were fatally damaged was 8.8%, 23.7%, and 23.7% in cases 1, 2, 3 respectively. In addition, the percentage of plants that were minimally damaged was 17.6%, 20%, and 25.9% in cases 1, 2,3 respectively.