This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic stud...This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic studies about weed identification.展开更多
Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield.Therefore,the study explores corn identification and positioning methods based on machine vision.The ultra-green fea...Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield.Therefore,the study explores corn identification and positioning methods based on machine vision.The ultra-green feature algorithm and maximum betweenclass variance method(OTSU)were used to segment maize corn,weeds,and land;the segmentation effect was significant and can meet the following shape feature extraction requirements.Finally,the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method.The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h,the recognition accuracy can reach 94.1%.The technique used in this study is accessible for normal cases and can make a good recognition effect;the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.展开更多
Weeds normally grow in patches and spatially distributed in field. Patch spraying to control weeds has advantages of chemical saving, reduced cost and environmental pollution. Advent of electro-optical sensing capabil...Weeds normally grow in patches and spatially distributed in field. Patch spraying to control weeds has advantages of chemical saving, reduced cost and environmental pollution. Advent of electro-optical sensing capabilities has paved the way of using machine vision technologies for patch spraying. Machine vision system has to acquire and process digital images to make control decisions. Proper identification and classification of objects present in image holds the key to make control decisions and use of any spraying operation performed. Recognition of objects in digital image may be affected by background, intensity, image resolution, orientation of the object and geometrical characteristics. A set of 16, including 11 shape and 5 texture-based parameters coupled with predictive discriminating analysis has been used to identify the weed leaves. Geometrical features were indexed successfully to eliminate the effect of object orientation. Linear discriminating analysis was found to be more effective in correct classification of weed leaves. The classification accuracy of 69% to 80% was observed. These features can be utilized for development of image based variable rate sprayer.展开更多
Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management ...Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management contributing to better crop yields.Intelligent and smart spot-spraying system's efficiency relies on the accuracy of the computer vision based detectors for autonomous weed control.In the present study,feasibility of deep learning based techniques(Alexnet,GoogLeNet,InceptionV3,Xception)were evaluated in weed identification from RGB images of bell pepper field.The models were trained with different values of epochs(10,20,30),batch sizes(16,32),and hyperparameters were tuned to get optimal performance.The overall accuracy of the selected models varied from 94.5 to 97.7%.Among the models,InceptionV3 exhibited superior performance at 30-epoch and 16-batch size with a 97.7%accuracy,98.5%precision,and 97.8%recall.For this Inception3 model,the type 1 error was obtained as 1.4%and type II error was 0.9%.The effectiveness of the deep learning model presents a clear path towards integrating them with image-based herbicide applicators for precise weed management.展开更多
为解决光线遮蔽、藻萍干扰以及稻叶尖形状相似等复杂环境导致稻田杂草识别效果不理想问题,该研究提出一种基于组合深度学习的杂草识别方法。引入MSRCP(multi-scale retinex with color preservation)对图像进行增强,以提高图像亮度及对...为解决光线遮蔽、藻萍干扰以及稻叶尖形状相似等复杂环境导致稻田杂草识别效果不理想问题,该研究提出一种基于组合深度学习的杂草识别方法。引入MSRCP(multi-scale retinex with color preservation)对图像进行增强,以提高图像亮度及对比度;加入ViT分类网络去除干扰背景,以提高模型在复杂环境下对小目标杂草的识别性能。在YOLOv7模型中主干特征提取网络替换为GhostNet网络,并引入CA注意力机制,以增强主干特征提取网络对杂草特征提取能力及简化模型参数计算量。消融试验表明:改进后的YOLOv7模型平均精度均值为88.2%,较原YOLOv7模型提高了3.3个百分点,参数量减少10.43 M,计算量减少66.54×109次/s。识别前先经过MSRCP图像增强后,与原模型相比,改进YOLOv7模型的平均精度均值提高了2.6个百分点,光线遮蔽、藻萍干扰以及稻叶尖形状相似的复杂环境下平均精度均值分别提高5.3、3.6、3.1个百分点,加入ViT分类网络后,较原模型平均精度均值整体提升了4.4个百分点,光线遮蔽、藻萍干扰一级稻叶尖形状相似的复杂环境下的平均精度均值较原模型整体提升了6.2、6.1、5.7个百分点。ViT-改进YOLOv7模型的平均精度均值为92.6%,相比于YOLOv5s、YOLOXs、MobilenetV3-YOLOv7、YOLOv8和改进YOLOv7分别提高了11.6、10.1、5.0、4.2、4.4个百分点。研究结果可为稻田复杂环境的杂草精准识别提供支撑。展开更多
文摘This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic studies about weed identification.
基金the National Key Research and Development Program of China[Grant numbers:2019YFB1312303].
文摘Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield.Therefore,the study explores corn identification and positioning methods based on machine vision.The ultra-green feature algorithm and maximum betweenclass variance method(OTSU)were used to segment maize corn,weeds,and land;the segmentation effect was significant and can meet the following shape feature extraction requirements.Finally,the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method.The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h,the recognition accuracy can reach 94.1%.The technique used in this study is accessible for normal cases and can make a good recognition effect;the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.
文摘Weeds normally grow in patches and spatially distributed in field. Patch spraying to control weeds has advantages of chemical saving, reduced cost and environmental pollution. Advent of electro-optical sensing capabilities has paved the way of using machine vision technologies for patch spraying. Machine vision system has to acquire and process digital images to make control decisions. Proper identification and classification of objects present in image holds the key to make control decisions and use of any spraying operation performed. Recognition of objects in digital image may be affected by background, intensity, image resolution, orientation of the object and geometrical characteristics. A set of 16, including 11 shape and 5 texture-based parameters coupled with predictive discriminating analysis has been used to identify the weed leaves. Geometrical features were indexed successfully to eliminate the effect of object orientation. Linear discriminating analysis was found to be more effective in correct classification of weed leaves. The classification accuracy of 69% to 80% was observed. These features can be utilized for development of image based variable rate sprayer.
文摘Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management contributing to better crop yields.Intelligent and smart spot-spraying system's efficiency relies on the accuracy of the computer vision based detectors for autonomous weed control.In the present study,feasibility of deep learning based techniques(Alexnet,GoogLeNet,InceptionV3,Xception)were evaluated in weed identification from RGB images of bell pepper field.The models were trained with different values of epochs(10,20,30),batch sizes(16,32),and hyperparameters were tuned to get optimal performance.The overall accuracy of the selected models varied from 94.5 to 97.7%.Among the models,InceptionV3 exhibited superior performance at 30-epoch and 16-batch size with a 97.7%accuracy,98.5%precision,and 97.8%recall.For this Inception3 model,the type 1 error was obtained as 1.4%and type II error was 0.9%.The effectiveness of the deep learning model presents a clear path towards integrating them with image-based herbicide applicators for precise weed management.
文摘为解决光线遮蔽、藻萍干扰以及稻叶尖形状相似等复杂环境导致稻田杂草识别效果不理想问题,该研究提出一种基于组合深度学习的杂草识别方法。引入MSRCP(multi-scale retinex with color preservation)对图像进行增强,以提高图像亮度及对比度;加入ViT分类网络去除干扰背景,以提高模型在复杂环境下对小目标杂草的识别性能。在YOLOv7模型中主干特征提取网络替换为GhostNet网络,并引入CA注意力机制,以增强主干特征提取网络对杂草特征提取能力及简化模型参数计算量。消融试验表明:改进后的YOLOv7模型平均精度均值为88.2%,较原YOLOv7模型提高了3.3个百分点,参数量减少10.43 M,计算量减少66.54×109次/s。识别前先经过MSRCP图像增强后,与原模型相比,改进YOLOv7模型的平均精度均值提高了2.6个百分点,光线遮蔽、藻萍干扰以及稻叶尖形状相似的复杂环境下平均精度均值分别提高5.3、3.6、3.1个百分点,加入ViT分类网络后,较原模型平均精度均值整体提升了4.4个百分点,光线遮蔽、藻萍干扰一级稻叶尖形状相似的复杂环境下的平均精度均值较原模型整体提升了6.2、6.1、5.7个百分点。ViT-改进YOLOv7模型的平均精度均值为92.6%,相比于YOLOv5s、YOLOXs、MobilenetV3-YOLOv7、YOLOv8和改进YOLOv7分别提高了11.6、10.1、5.0、4.2、4.4个百分点。研究结果可为稻田复杂环境的杂草精准识别提供支撑。
