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Precision spraying can significantly reduce herbicide input for turf weed management. A major challenge for autonomous precision herbicide spraying is to accurately and reliably detect weeds growing in turf. Deep convolutional neural networks (DCNNs), an important artificial intelligent tool, demonstrated extraordinary capability to learn complex features from images. The feasibility of using DCNNs, including various image classification or object detection neural networks, has been investigated to detect weeds growing in turf. Due to the high level of performance of weed detection, DCNNs are suitable for the ground-based detection and discrimination of weeds growing in turf. However, reliable weed detection may be subject to the influence of weeds (e.g., biotypes, species, densities, and growth stages) and turf factors (e.g., turf quality, mowing height, and dormancy vs. non-dormancy). The present review article summarizes the previous research findings using DCNNs as the machine vision decision system of smart sprayers for precision herbicide spraying, with the aim of providing insights into future research. [ABSTRACT FROM AUTHOR]

Background: Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides. Results: GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F1 scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated. Conclusion: These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers. [ABSTRACT FROM AUTHOR]

BACKGROUND Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at various growth stages and densities. This paper presents a novel deep‐learning‐based method for weed detection that recognizes vegetable crops and classifies all other green objects as weeds. RESULTS: The optimal confidence threshold values for YOLO‐v3, CenterNet, and Faster R‐CNN were 0.4, 0.6, and 0.4/0.5, respectively. These deep‐learning models had average precision (AP) above 97% in the testing dataset. YOLO‐v3 was the most accurate model for detection of vegetables and yielded the highest F1 score of 0.971, along with high precision and recall values of 0.971 and 0.970, respectively. The inference time of YOLO‐v3 was similar to CenterNet, but significantly shorter than that of Faster R‐CNN. Overall, YOLO‐v3 showed the highest accuracy and computational efficiency among the deep‐learning architectures evaluated in this study. CONCLUSION: These results demonstrate that deep‐learning‐based methods can reliably detect weeds in vegetable crops. The proposed method avoids dealing with various weed species, and thus greatly reduces the overall complexity of weed detection in vegetable fields. Findings have implications for advancing site‐specific robotic weed control in vegetable crops. [ABSTRACT FROM AUTHOR]

Deep learning-based methods for weed detection and precise herbicide application are promising for reducing herbicide input and weed control costs. However, training the neural network to recognize weeds requires annotating a large number of training images, which is time-consuming and labor-intensive. In addition, in turf sod farms, sods need to be periodically harvested, leading to varying turfgrass and bare soil areas, which increase the complexity of weed detection. To solve this problem, this research explored semi-supervised learning (SSL) methods to train image classification neural networks. The experiments were conducted to compare the training results using different SSL strategies with 100 and 200 labeled images at three image sizes of 240 × 240, 360 × 360, or 480 × 480 pixels. The training dataset images mainly contained purple nutsedge (Cyperus rotundus L.) and green kyllinga (Kyllinga brevifolia) at the pre-flowering or seedhead stage. The F 1 score, precision, and recall were used to evaluate the performance of the trained neural networks. The results showed that the network based on the FixMatch SSL strategy trained with the input images of 240 × 240 pixels exhibited the highest F 1 score, reaching 98.1% when trained with 100 labeled images and 98.2% when trained with 200 labeled images. To summarize, these results suggest that SSL achieved a great training performance with a small number of annotations. FixMatch SSL was the most effective neural network training strategy evaluated. For the weed detection task, it was observed that neural networks trained using an input image size of 240 × 240 pixels exhibited superior performance compared to the networks trained with other image sizes. In addition, employing the SSL method with only 200 labeled images enhanced the performance of the neural network, surpassing that of fully supervised learning (FSL) approaches. • The SSL strategy proved to be effective for weed detection with a small amount of labeled data. • The SSL strategy employed in FixMatch outperformed FSL. • The optimal performance of the trained model was achieved when utilizing input images at a resolution of 240 × 240 pixels. • Varying turf coverage and bare soil area barely had any meaningful impact on the performance of SSL for weed detection. [ABSTRACT FROM AUTHOR]

Machine vision-based precision herbicide application in wheat (Triticum aestivum L.) can substantially reduce herbicide input. However, detecting newly emerged weeds in wheat fields remains a challenging task. Current deep learning-based weed detection methods require the annotation of a large amount of data, which is both time-consuming and labor-intensive. To address this issue, this research improved a semi-supervised learning (SSL) algorithm based on consistency regularization and pseudo-labeling, and incorporated an attention mechanism. Compared to fully supervised learning (FSL) algorithms, the proposed method increased the classification accuracy by 16.5%, 17.84%, and 19.67% on datasets with 200 × 200, 300 × 300, and 400 × 400 pixel images, respectively, when only 100 labeled data per class were used. Overall, the developed machine vision models using the proposed method achieved weed detection with high accuracy while requiring much fewer labeled training images, and thus is more time and labor-efficient compared to an FSL algorithm. • A semi-supervised learning method was used for detecting weeds in wheat. • The addition of attention mechanisms improved the performance of weed detection. • The semi-supervised learning method out-performed fully supervised learning. • 200×200 pixels out-performed larger image sizes for semi-supervised learning. [ABSTRACT FROM AUTHOR]

Precision herbicide application can substantially reduce herbicide input, thereby cutting chemical costs and minimizing adverse environmental impacts. A smart sprayer prototype was designed and developed for precision herbicide application in turf. This is the first study evaluating the performances of a precision sprayer for weed control in turf in field conditions. The objectives of this research were to 1) evaluate and compare the performances of the traditional broadcast application and a newly developed precision spraying technology for control of weeds in dormant bermudagrass turf, and 2) investigate the influence of weed coverage on the spray volume requirement when using the precision spraying technology developed here. DenseNet, GoogLeNet, and ResNet were evaluated for discriminating the grid cells containing weeds (spray) with the grid cells containing bermudagrass turf exclusively (nonspray). All three neural networks had an F 1 score above 0.989 in the validation datasets. ResNet outperformed DenseNet and GoogLeNet with the highest F 1 scores (≥0.992) in the testing datasets. Applying herbicide only to turf areas infested with weeds saved a significant amount of the herbicide, while achieving the same level of weed control compared to the broadcast application. The developed precision spraying technology performed well and effectively reduced the amount of herbicide input applied to the dormant bermudagrass turf, compared to the broadcast herbicide application. Overall, the smart sprayer prototype developed in this research can be used for precision weed control in dormant turf, although its design needs to be further optimized. • A custom software was developed to detect and locate weeds using image classification neural networks. • A smart sprayer prototype was designed and developed for precision herbicide application on dormant bermudagrass turf. • The developed precision spraying technology effectively reduced the amount of herbicide input applied to the turf. [ABSTRACT FROM AUTHOR]

Precision application of specific herbicides to susceptible weeds can significantly save herbicide. This is the first study evaluating the performances of precision sprayer for weed control in turf based on the herbicide weed control spectrum in field conditions. The results showed that EfficientNet-v2 and ResNet never fall below 0.992 for discriminating and detecting the grid cells encompassing weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. MCPA, a synthetic auxin herbicide, is used to evaluate the performance of the developed smart sprayer for precision control of broadleaf weeds in dormant bermudagrass turf. The developed smart sprayer prototype detected and sprayed every grid cell containing broadleaf weeds in field experiments. Compared to the broadcast application, precision spraying of MCPA provided the same level of control of broadleaf weeds. By 18 days after treatment (DAT), the nontreated control had 13 weeds no. m−2, while the plots that received broadcast and precision spraying had 0 and 1 broadleaf weed plant no. m−2, respectively. Precision herbicide application according to the herbicide weed control spectrum (HWCS) with the developed smart sprayer provided the same level of broadleaf weed control and could save more herbicides compared to an approach without discriminating weed species. Overall, these findings clearly indicated that the developed smart sprayer prototype could effectively detect, discriminate, and spray herbicides onto the grid cells containing target weeds based on the HWCS. • Discriminating weed species based on their susceptibility to herbicides allows targeted herbicide application. • A smart sprayer prototype was designed and developed for precision herbicide application on turf. • Precision herbicide application with the smart sprayer achieved weed control equivalent to broadcast application. [ABSTRACT FROM AUTHOR]