Precision Weed Control System for Cotton

Lamm, Richard D.; Slaughter, David C.; Giles, D. K.

doi:10.13031/2013.7861

Cited by 35 publications

(14 citation statements)

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“…However, its accuracy was low, with only 47.6% of the weeds and 75.8% of the crops being successfully classified. Lamm et al (2002) developed a machine vision system to classify crops and weeds in commercial cotton fields based on their sizes, which Misclassification due to low camera frame rate. Li et al 10.3389/fpls.2023.1133969 improved the weed identification accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Detection methods based on characteristics of crop plants and weeds such as color ( Gupta and Ibaraki, 2014 ), size ( Lamm et al., 2002 ), and spectral reflectance ( Borregaard et al., 2000 ) have been proposed to classify crops and weeds. Blasco et al.…”

Section: Introductionmentioning

confidence: 99%

“…Detection methods based on characteristics of crop plants and weeds such as color (Gupta and Ibaraki, 2014), size (Lamm et al, 2002), and spectral reflectance (Borregaard et al, 2000) have been proposed to classify crops and weeds. Blasco et al (2002) developed a robotic system for weed control in transplanted lettuce, which identified the plants with the accuracies of 84% and 99% for weeds and lettuce based on the plant size, respectively.…”

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confidence: 99%

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A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control

Zhang

et al. 2023

Front. Plant Sci.

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Tomato is a globally grown vegetable crop with high economic and nutritional values. Tomato production is being threatened by weeds. This effect is more pronounced in the early stages of tomato plant growth. Thus weed management in the early stages of tomato plant growth is very critical. The increasing labor cost of manual weeding and the negative impact on human health and the environment caused by the overuse of herbicides are driving the development of smart weeders. The core task that needs to be addressed in developing a smart weeder is to accurately distinguish vegetable crops from weeds in real time. In this study, a new approach is proposed to locate tomato and pakchoi plants in real time based on an integrated sensing system consisting of camera and color mark sensors. The selection scheme of reference, color, area, and category of plant labels for sensor identification was examined. The impact of the number of sensors and the size of the signal tolerance region on the system recognition accuracy was also evaluated. The experimental results demonstrated that the color mark sensor using the main stem of tomato as the reference exhibited higher performance than that of pakchoi in identifying the plant labels. The scheme of applying white topical markers on the lower main stem of the tomato plant is optimal. The effectiveness of the six sensors used by the system to detect plant labels was demonstrated. The computer vision algorithm proposed in this study was specially developed for the sensing system, yielding the highest overall accuracy of 95.19% for tomato and pakchoi localization. The proposed sensor-based system is highly accurate and reliable for automatic localization of vegetable plants for weed control in real time.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control

Zhang

et al. 2023

Front. Plant Sci.

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“…Various VIs have been widely used for qualitative and quantitative evaluation of vegetation cover and crop growth dynamics [36][37][38]. Based on the relevant literature [2,[17][18][19][20][21][22][23][24][25][39][40][41][42][43][44][45][46], nine commonly used nitrogen content prediction vegetation indices were selected in this study with the formula shown in Table 2.…”

Section: Vegetation Indices Selectionmentioning

confidence: 99%

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Guo

Huang

et al. 2022

Drones

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UAV imaging provides an efficient and non-destructive tool for characterizing farm information, but the quality of the UAV model is often affected by the image’s spatial resolution. In this paper, the predictability of models established using UAV multispectral images with different spatial resolutions for nitrogen content of winter wheat was evaluated during the critical growth stages of winter wheat over the period 2021–2022. Feature selection based on UAV image reflectance, vegetation indices, and texture was conducted using the competitive adaptive reweighted sampling, and the random forest machine learning method was used to construct the prediction model with the optimized features. Results showed that model performance increased with decreasing image spatial resolution with a R2, a RMSE, a MAE and a RPD of 0.84, 4.57 g m−2, 2.50 g m−2 and 2.34 (0.01 m spatial resolution image), 0.86, 4.15 g m−2, 2.82 g m−2 and 2.65 (0.02 m), and 0.92, 3.17 g m−2, 2.45 g m−2 and 2.86 (0.05 m), respectively. Further, the transferability of models differed when applied to images with coarser (upscaling) or finer (downscaling) resolutions. For upscaling, the model established with the 0.01 m images had a R2 of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively. For downscaling, the model established with the 0.05 m image features had a R2 of 0.86 and 0.83 when applied to images of 0.01 m and 0.02 m resolutions. Though the image spatial resolution affects image texture features more than the spectral features and the effects of image spatial resolution on model performance and transferability decrease with increasing plant wetness under irrigation treatment, it can be concluded that all the UAV images acquired in this study with different resolutions could achieve good predictions and transferability of the nitrogen content of winter wheat plants.

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“…Lee, Slaughter and Giles (1999) developed a real-time intelligent robotic system by using machine vision consisting of an array of eight nozzles for precision weed control in tomato seed lines. Lamm, Slaughter and Giles (2002) developed and tested a robotic weed control system for cotton plants. Berenstein and Edan (2017) presented a human-robot collaborative sprayer designed for site-specific targeted spraying.…”

Section: Introductionmentioning

confidence: 99%

Design and development of a servo-controlled target-oriented robotic micro-dose spraying system in precision weed control

Özlüoymak

2021

Semina: Ciênc. Agrár.

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The broadcast spraying method using excessive amounts of pesticides is generally preferred for weed control in agriculture. In this study, a mobile robot was developed and tested on artificial weed targets for a micro-dose spraying system to reduce amount of liquid sprayed for weed control. A prototype mobile robot consisting of a robotic platform, machine vision and steerable spraying unit was constructed and controlled by using LabVIEW software and tested to evaluate the applicability of the spraying system. The greenness method and segmentation algorithm were used to extract artificial weeds from the background. The artificial weed samples were treated according to their coordinates by using a servo-based micro-dose spraying needle nozzle. The experiments were carried out at speeds of 0.42, 0.54, 0.66, 0.78 and 0.90 km h-1 to evaluate the performance of the spraying system under laboratory conditions. The tracking and targeting performances of the mobile spraying system were observed visually. Consumption, deposition and coverage rate experiments were carried out by using graduated cups, filter papers and water-sensitive papers to evaluate the spraying efficiency of the system under 200 kPa of spraying pressure. The results showed that the targeted micro-dose spraying method saved approximately 95% of the application volume compared with the broadcast spraying method. Higher spraying efficiency was determined at the middle locations rather than at the edge locations according to the amount of deposition and coverage rate results. The servo-controlled target-oriented weed control system that was developed was tested experimentally and found to be very efficient.

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Precision Weed Control System for Cotton

Cited by 35 publications

References 0 publications

A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control

A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

Design and development of a servo-controlled target-oriented robotic micro-dose spraying system in precision weed control

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