Search Results (17)

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field. Full article

Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology. Full article

Weeds compete with crops for water and nutrients and can adversely affect crop growth and yield, so it is important to research effective weed control methods. This paper provides an overview of the impact of weeds on crop yield and describes the current state of research on weed management in field herbaceous crops. Physical weed control mainly refers to thermal technologies represented by flame weed control and laser weed control, which can efficiently and accurately remove weeds. Mechanical weed control requires a combination of sensor technologies, machine vision technology, and high-precision navigation to improve weed control accuracy. Biological weed control relies heavily on plant extracts and pathogens to create herbicides, but it is costly, and some can be toxic to mammals. Chemical weed control is a common method, resulting in environmental pollution and weed resistance. To reduce the use of chemical herbicides, scholars have proposed integrated weed management strategies, which combine biological control, control of the seed bank, and improve crop competitiveness. Integrated weed management strategies are considered to be the future direction of weed management. In conclusion, physical, mechanical, biological, and chemical weed control methods are commonly used in weed management. Each method has its applicable scenarios, and the implementation of integrated weed management strategies can lead to better weed control, improving crop yield and quality. The main objective of this review is to organize the research progress on weed management methods for herbaceous crops in the field and to provide a reference for the agricultural sector to develop weed control strategies. Specifically, this paper categorizes weed management methods into four groups, discusses and presents the advantages and disadvantages of the aforementioned weed control methods, and discusses future research directions. Full article

This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles—25%, 50%, 75%, and 100%—showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system. Full article

This article presents a division of methods to support mechanical weeding based on mechatronic control systems and estimates their effectiveness. The subject was undertaken due to the noticeable increase in interest in machine weeding methods, which is the result of the need for farmers to meet the growing awareness of customers focusing on healthy and high-quality products and the European Union policy promoting environmental protection programs, such as the European Green Deal and supporting commission priorities like the Mission Soil as a flagship initiative of the long-term vision for the EU’s rural areas. Mechanical weeding meets the stringent conditions set by organic farming, and automation favours the development of these methods. Based on sources in the literature, it has been shown that it is possible to increase the weeding speed by at least 1.6 times by using the tool position correction system for row crops. In the case of crops requiring weeding, and in the spaces between plants in a row, the use of specialised weeding machines allows for an increase in the weeding efficiency by up to 2.57 times compared to manual weeding. Each of the analysed methods used to support weeding are subject to a certain error due to the use of sources in the literature, including manufacturers’ materials; however, it shows an upward trend in the effectiveness of using mechatronic weeding support systems, which was part of the thesis. This article presents the division of these systems and analyses the specific market solutions of machines, which is its distinguishing feature. Full article

A search for energy efficient, non-herbicide weed control methods led to development of a novel electrical weeding technology. This study focuses on weed control efficiency and energy as elements of a system that would include machine vision and robotics to control escape weeds in field crops. Two pulse generation systems, one single and one multiple, were developed and evaluated at different delivered voltages and energies. Greenhouse trials using specially designed and built application and recording technology showed the application of precisely applied micro-shocks with precisely controlled direct current (DC) voltage, pulse number, pulse length and period (hereafter PMS) can kill small Lolium multiflorum Lam., Chenopodium album L., Amaranthus powellii S. Wats. and Solanum nigrum L. plants with minimal energy. Plants took as much as two weeks to die. Increasing applied energy increased effectiveness as determined by plant biomass reduction and death rate. Grasses appear difficult to control once tillering has commenced, and high voltages may destroy leaf blades but not growing points. Broadleaved plants took several days to show evidence of chlorosis which preceded senescence and death. Our results showed that 5 J is sufficient energy to bring about death or severe growth limitation in many seedlings up to 15 cm height. This is as little as 1% of the energy of, and more effective than, ultra-low energy treatments reported in other recent research. To control five herbicide resistant weeds m⁻², the required energy would be about 0.25 MJ ha⁻¹ plus transport and actuation energy for weed destruction, as compared to an optimum target of about 20–40 MJ ha⁻¹ including transport suggested in the literature. PMS can effectively control broadleaved weed seedlings and small non-tillering grasses at a fraction of the energy required by commercially available systems. This indicates PMS has potential as a viable technology for hand-held electric weeders or as part of a site-specific robotic weeding system. Full article

Urban agriculture can be shortly defined as the growing of plants and/or the livestock husbandry in and around cities. Although it has been a common occupation for the urban population all along, recently there is a growing interest in it both from public bodies and researchers, as well as from ordinary citizens who want to engage in self-cultivation. The modern citizen, though, will hardly find the free time to grow his own vegetables as it is a process that requires, in addition to knowledge and disposition, consistency. Given the above considerations, the purpose of this work was to develop an economic robotic system for the automatic monitoring and management of an urban garden. The robotic system was designed and built entirely from scratch. It had to have suitable dimensions so that it could be placed in a balcony or a terrace, and be able to scout vegetables from planting to harvest and primarily conduct precision irrigation based on the growth stage of each plant. Fertigation and weed control will also follow. For its development, a number of technologies were combined, such as Cartesian robots’ motion, machine vision, deep learning for the identification and detection of plants, irrigation dosage and scheduling based on plants’ growth stage, and cloud storage. The complete process of software and hardware development to a robust robotic platform is described in detail in the respective sections. The experimental procedure was performed for lettuce plants, with the robotic system providing precise movement of its actuator and applying precision irrigation based on the specific needs of the plants. Full article

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized. Full article

Automation continues to play a greater role in agricultural production with commercial systems now available for machine vision identification of weeds and other pests, autonomous weed control, and robotic harvesters for fruits and vegetables. The growing availability of autonomous machines in agriculture indicates that there are opportunities to increase automation in cotton production. This article considers how current and future advances in automation has, could, or will impact cotton production practices. The results are organized to follow the cotton production process from land preparation to planting to within season management through harvesting and ginning. For each step, current and potential opportunities to automate processes are discussed. Specific examples include advances in automated weed control and progress made in the use of robotic systems for cotton harvesting. Full article

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic. Full article

Harrowing is mostly applied with a constant intensity across the whole field. Heterogeneous field conditions such as variable soil texture, different crop growth stages, variations of the weed infestation level, and weed species composition are usually not considered during the treatment. This study offers a new approach to sensor-based harrowing which addresses these field variations. Smart harrowing requires the continuous adaptation of the treatment intensity to maintain the same level of crop selectivity while ensuring a high weed control efficacy. Therefore, a harrow was equipped with a sensor-system to automatically adjust the angle of the harrow tines based on a newly developed decision algorithm. In 2020, three field experiments were conducted in winter wheat and spring oats to investigate the response of the weed control efficacy and the crop to different harrowing intensities, in Southwest Germany. In all experiments, six levels of crop soil cover (CSC) were tested. The CSC determines the balance between crop damage and weed removal. Each experiment contained an untreated control and an herbicide treatment as a comparison to the harrowing treatments. The results showed an increase in the weed control efficacy (WCE) with an increasing CSC threshold. Difficult-to-control weed species such as Cirsium arvense L. and Galium aparine L. were best controlled with a CSC threshold of 70%. However, 70% CSC caused up to 50% crop biomass loss and up to 2 t·ha⁻¹ of grain yield reduction. With a CSC threshold of 20% it was possible to control up to 98% of Thlaspi arvense L. The highest crop biomass, grain yield, and selectivity were achieved with an CSC threshold of 20–25% at all locations. With this harrowing intensity, grain yields were higher than in the herbicide plots and a WCE of 68–98% was achieved. Due to the rapid adjustment of tine angle, the new sensor-based harrow allows users to apply the most selective harrowing intensity in every location of the field. Therefore, it can achieve equal weed control efficacies as using herbicide applications. Full article

Conventional cultivation works to control weeds between the rows, but it ignores the weeds in crop rows which are most competitive with crops. Many vegetable crops still require manual removal of intra-row weeds not otherwise controlled by herbicides or conventional cultivation. The increasing labor costs of weed control and the continued emergences of herbicide-resistant weeds are threatening grower ability to manage weeds and maintain profitability. Intra-row weeders are commercially available but work best in low weed populations. One strategy for rapid weed crop differentiation is to utilize a machine-detectable compound to mark a crop. This paper proposes a new systemic plant signaling technology that can create machine-readable crops to facilitate the automated removal of intra-row weeds in early growth stages. Rhodamine B (Rh–B) is an efficient systemic compound to label crop plants due to its membrane permeability and unique fluorescent properties. The project involves applying solutions of Rh–B at 60 ppm to the roots of lettuce and tomato plants prior to transplantation to evaluate Rh–B persistence in plants under different levels of sunlight. Lettuce and tomato seedlings with the systemic Rh–B should be reliably recognized during their early growth stages. An intelligent robot is expected to be developed to identify the locations of plants based on the systemic signal inside. Reduced light treatments should help to alleviate the photodegradation of Rh–B in plants. After being exposed to full sunlight for 27 days, the systemic Rh–B would be detectable in tomato branches and lettuce ribs, and these plants are tolerant to root treatments with this fluorescent compound. This paper describes the project background and plan as well as the anticipated contributions of the research to allow the machine vision system to reliably identify the crop plants, and thus showing technical feasibility for outdoor weed control. Full article

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications. Full article

This paper proposes a scheme that combines computer vision and multi-tasking processes to develop a small-scale smart agricultural machine that can automatically weed and perform variable rate irrigation within a cultivated field. Image processing methods such as HSV (hue (H), saturation (S), value (V)) color conversion, estimation of thresholds during the image binary segmentation process, and morphology operator procedures are used to confirm the position of the plant and weeds, and those results are used to perform weeding and watering operations. Furthermore, the data on the wet distribution area of surface soil (WDAS) and the moisture content of the deep soil is provided to a fuzzy logic controller, which drives pumps to perform variable rate irrigation and to achieve water savings. The proposed system has been implemented in small machines and the experimental results show that the system can classify plant and weeds in real time with an average classification rate of 90% or higher. This allows the machine to do weeding and watering while maintaining the moisture content of the deep soil at 80 ± 10% and an average weeding rate of 90%. Full article

This paper presents a machine vision retrofit system designed for upgrading used tractors to allow the control of the tillage implements and enable real-time field operation. The retrofit package comprises an acquisition system placed in the cabin, a front-mounted RGB camera sensor, and a rear-mounted Peiseler encoder wheel. The method combines shape analysis and colorimetric k-nearest neighbor (k-NN) clustering for in-field weed discrimination. This low-cost retrofit package can use interchangeable sensors, supplying flexibility of use with different farming implements. Field tests were conducted within lettuce and broccoli crops to develop the image analysis system for the autonomous control of an intra-row hoeing implement. The performance showed by the system in the trials was judged in terms of accuracy and speed. The system was capable of discriminating weed plants from crop with few errors, achieving a fairly high performance, given the severe degree of weed infestation encountered. The actuation time for image processing, currently implemented in MATLAB integrated with the retrofit kit, was about 7 s. The correct detection rate was higher for lettuce (from 69% to 96%) than for broccoli (from 65% to 79%), also considering the negative effect of shadows. To be implementable, the experimental code needs to be optimized to reduce acquisition and processing times. A software utility was developed in Java to reach a processing time of two images per second. Full article