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
Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,h...Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,have been widely used but come with their own set of challenges.These methods are often time-consuming,labor-intensive,and pose environmental risks.Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness.However,over-reliance on herbicides has led to environmental contamination,weed resistance,and potential health hazards.To address these issues,researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies.As technology advances,there is a growing interest in exploring innovative and sustainable weed management approaches.This review examines the potential of machine learning in chemical weed management.Machine learning offers innovative and sustainable approaches by analyzing large data sets,recognizing patterns,and making accurate predictions.Machine learning models can classify weed species and optimize herbicide usage.Real-time monitoring enables timely intervention,preventing invasive species spread.Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices,reducing herbicide usage and minimizing environmental impact.Validation and refinement of these algorithms are needed for practical application.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
Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos.This technology plays a crucial role in facilitating the trans...Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos.This technology plays a crucial role in facilitating the transition from conventional to precision agriculture,particularly in the context of weed control.Precision agriculture,which previously relied on manual efforts,has now embraced the use of smart devices for more efficient weed detection.However,several challenges are associated with weed detection,including the visual similarity between weed and crop,occlusion and lighting effects,as well as the need for early-stage weed control.Therefore,this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning,as well as the combination of the two methods,for weed detection across different crop fields.The results of this review show the advantages and disadvantages of using machine learning and deep learning.Generally,deep learning produced superior accuracy compared to machine learning under various conditions.Machine learning required the selection of the right combination of features to achieve high accuracy in classifyingweed and crop,particularly under conditions consisting of lighting and early growth effects.Moreover,a precise segmentation stage would be required in cases of occlusion.Machine learning had the advantage of achieving real-time processing by producing smaller models than deep learning,thereby eliminating the need for additional GPUs.However,the development of GPU technology is currently rapid,so researchers are more often using deep learning for more accurate weed identification.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.
文摘Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,have been widely used but come with their own set of challenges.These methods are often time-consuming,labor-intensive,and pose environmental risks.Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness.However,over-reliance on herbicides has led to environmental contamination,weed resistance,and potential health hazards.To address these issues,researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies.As technology advances,there is a growing interest in exploring innovative and sustainable weed management approaches.This review examines the potential of machine learning in chemical weed management.Machine learning offers innovative and sustainable approaches by analyzing large data sets,recognizing patterns,and making accurate predictions.Machine learning models can classify weed species and optimize herbicide usage.Real-time monitoring enables timely intervention,preventing invasive species spread.Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices,reducing herbicide usage and minimizing environmental impact.Validation and refinement of these algorithms are needed for practical application.
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Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos.This technology plays a crucial role in facilitating the transition from conventional to precision agriculture,particularly in the context of weed control.Precision agriculture,which previously relied on manual efforts,has now embraced the use of smart devices for more efficient weed detection.However,several challenges are associated with weed detection,including the visual similarity between weed and crop,occlusion and lighting effects,as well as the need for early-stage weed control.Therefore,this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning,as well as the combination of the two methods,for weed detection across different crop fields.The results of this review show the advantages and disadvantages of using machine learning and deep learning.Generally,deep learning produced superior accuracy compared to machine learning under various conditions.Machine learning required the selection of the right combination of features to achieve high accuracy in classifyingweed and crop,particularly under conditions consisting of lighting and early growth effects.Moreover,a precise segmentation stage would be required in cases of occlusion.Machine learning had the advantage of achieving real-time processing by producing smaller models than deep learning,thereby eliminating the need for additional GPUs.However,the development of GPU technology is currently rapid,so researchers are more often using deep learning for more accurate weed identification.
