Weed discrimination using ultrasonic sensors

2010

The spatial distribution of weeds in crop fields is heterogeneous. Therefore, limiting herbicides application to weed infested areas would lead to economical and environmental benefits. For real-time spot treatments, sensors detecting weed patches are needed. Vegetation sensors could be used in the inter-rows to trigger herbicide spraying on both rows and inter-rows if weed cover on and between the crop rows is uniform. To verify this hypothesis, weed cover on and between corn rows was evaluated using photographs acquired in corn fields at the 3 to 5 leaf stage. A one hectare plot was sampled in 2004, 2005 and 2007 at one location and nine one hectare plots were sampled in corn fields dispersed across the province of Quebec (Canada) in 2008. All fields were planted in corn under conventional tillage (75 cm row spacing). A segmentation algorithm was used to isolate vegetation pixels. Samples for the analysis consisted of 23 x 750 mm strips free of corn plants and covering three regio...

International Journal of Chemical Studies, 2020

Undesirable and unwanted plants grow autonomously, nonuniformly in farmland and compete with the beneficial crop called a weed. It strives with the crop for nutrients, sunlight, water, space and grows at a faster rate. This results in a decreased growth rate of crop seedlings, make them susceptible to pests and diseases, eventually responsible for crop yield reduction and pertains to the poor economic condition of farmers as well as the nation. Hence, weed control is very crucial in crop production. Several studies have documented the yield loss associated with weed competition. Limiting factors of general weed control methods create the situation for design-development of new approaches based on robotics, automation and sensor techniques. Many research studies documented various weed discrimination, identification and control mechanisms in the fields. The automatic distinction between crop-weed has its own importance in weed control applications. Sensor-based approaches, machine vision systems, RTK GPS based systems, etc. are found better to achieve effective weed control and helps in improving crop yield. Robotic technology could provide a means to reduce current dependency of agriculture on chemical herbicides, strengthening its sustainability, and minimizing environmental impacts. These new technologies hold promise towards the improvement of agriculture's few remaining unmechanized and drudging tasks. This paper reviews the robotics-automation and sensor-based approaches in the detection of weeds and their control strategies.

Weed Research, 2009

Site-specific weed control technologies are defined as machinery or equipment embedded with technologies that detect weeds growing in a crop and, taking into account predefined factors such as economics, take action to maximise the chances of successfully controlling them. In this study, we describe the basic parts of site-specific weed control technologies, comprising weed sensing systems, weed management models and precision weed control implements. A review of state-of-the-art technologies shows that several weed sensing systems and precision implements have been developed over the last two decades, although barriers prevent their break-through. Most important among these is the lack of a truly robust weed recognition method, owing to mutual shading among plants and limitations in the capacity of highly accurate spraying and weeding apparatus. Another barrier is the lack of knowledge about the economic and environmental potential for increasing the resolution of weed control. The integration of site-specific information on weed distribution, weed species composition and density and the effect on crop yield, is decisive for successful site-specific weed management.

EURASIP Journal on Advances in Signal Processing, 2002

This study concerns the detection and localization of weed patches in order to improve the knowledge on weed-crop competition. A remote control aircraft provided with a camera allowed to obtain low cost and repetitive information. Different processings were involved to detect weed patches using spatial then spectral methods. First, a shift of colorimetric base allowed to separate the soil and plant pixels. Then, a specific algorithm including Gabor filter was applied to detect crop rows on the vegetation image. Weed patches were then deduced from the comparison of vegetation and crop images. Finally, the development of a multispectral acquisition device is introduced. First results for the discrimination of weeds and crops using the spectral properties are shown from laboratory tests. Application of neural networks were mostly studied.

Spectral characteristics of stems and leaves of various crop and weed species were studied using a diode-array spectrometer. Five feature wavelengths were selected to form color indices as input variables to a classification model for weed detection. The feature wavelengths also served as the basis for design of an optical weed sensor. Based on experimental data, color indices insensitive to illumination variations were designed and tested on the sensor. Laboratory tests showed that the sensor identified wheat, bare soil, and weeds (several species combined) with classification rates of 100%, 100%, and 71.6%, respectively, for the training data set when the weed density was above 0.02 plants/cm 2 . The classification rates for the validation data set were 73.8%, 100%, and 69.9%, respectively. When the density of weeds was low, as in the case of a single weed plant, more than 50% of the weeds were misclassified as soil. Misclassifications between wheat and weeds were not observed at any weed and wheat densities tested. raditional approaches to herbicide application are based on the assumption that weeds are distributed uniformly in fields. However, most agricultural fields are spatially variable in weed infestation to a certain degree. The distribution of weeds, particularly grass weeds in cereal crops, is often "patchy," rather than even or random. pointed out that portions of cereal crop fields are free of weeds, and weed species found in different fields of the same crop are often different. The efficiency of weed control can be improved if herbicides are applied only over the weed-infested areas. A precision weed sensor combined with selective spray has great potential to improve the efficiency.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

In this world, agriculture is a vital and necessary component of human nutrition. The majority of people in the world work in agriculture. The economy's agricultural sector is important. Also, it is crucial to reduce prices while raising the calibre of agricultural output. Modern agriculture needs to be more productive in order to meet demand and supply in light of the expanding population. When weeds detected in plants are taken into consideration, it is revealed that only a small percentage of weed plants are poisonous. Certain weeds are poisonous, which has a negative impact on livestock and eventually reduces crop output. But how can one assess whether or not a particular agricultural plant is dangerous? This makes weed identification crucial.

2020

This analysis has been supported by the employment of preciseness agriculture tools for the management of weeds in crops. It has focused on the creation of an image processing formula to sight the existence of weeds in an exceedingly specific website of crops. The most important objective has been to get formula so a weed detection system will be developed through binary classifications. The initial step of the image process is the detection of inexperienced plants to eliminate all the soil within the image, reducing data that are not necessary. Then, it's targeted on the vegetation by segmentation and eliminating unwanted data through medium and morphological filters. Finally, labeling objects have been created in the image so weed detection may be done employing a threshold based on the world of detection. This formula establishes correct observance of weeds and may be enforced in automated systems for the obliteration of weeds in crops, either through the employment of machin...

The ideal setup for any weed sprayer system would be to have at disposal a tool providing information on the amount of weed and crop present at each point in realtime, while the tractor mounting the spraying bar is moving. This paper presents a computer vision system that successfully discriminate in real-time between weeds and crop in outdoor field images under varying light, soil background texture and crop damage conditions. The system was tested on several maize videos taken during different years, yielding very satisfactory results.

In agriculture fields, weed is threat for growth of crops. One way of controlling/removing the weeds is through spraying herbicides. Spraying herbicides can either be manual or automatic (using robots). Manual spraying herbicide requires man power and it is time consuming and in current scenario everyone looks to it that work has to be done quickly. The solution for this problem is the automatic method. We have many existing techniques to differentiate any given two objects using different algorithms. Spot spraying uses more amounts of weedicides which causes the wastage of weedicides and therefore money, also the quality of the soil underneath the crops will be in vain. Thus to prevent all these wastages, this idea is of the automated weed seeker which will not only minimize the use of weedicides but also maintains the soil richness of the fields. In this paper two algorithms have been stated, one is area thresholding and the other is color segmentation. These algorithms have been ...

Weed control is a significant cost for speciality crop producers, especially on organic farms. Agricultural operations are still largely dependent on hand weeding that is labour intensive and labour shortages and rising wages have led to a surge in food production costs. Thus, there is an inherent need to automate weed control and contain both labour costs and demands. Automatically distinguishing weeds from the crop plant is a complex problem since weeds come in a wide variety of colours, shapes, and sizes, and crop plant foliage is often overlapped with itself or occluded by the weeds. Current technology in commercial use, cannot reliably and effectively perform the differentiation task in such complex scenarios in real-time. As a solution to this problem, our team at the University of California, Davis has developed a novel concept called crop signalling, a technology to make crop plants machine readable and reliably distinguishable from weeds for automatic weed control. Four different techniques have been investigated and developed to make smart crop marking systems such as a) systemic markers, b) fluorescent proteins, c) plant labels and d) topical markers. Indoor experiments have been conducted for each method. Field experiments, using plant labels and the topical markers methods, have been successfully conducted for real-time weed control in tomato and lettuce. The results demonstrated that robots could automatically detect and distinguish 99.7% of the crop plants with no false positive errors in dense complex outdoor scenes with high weed densities. The crop/weed differentiation was thus effective, fast, reliable, and commercialisation of robotic weed control using the technique may be feasible.