The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The...The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.展开更多
In this study an effort has been made to use plant polyphenol oxidases; potato （Solanum tuberosum） and brinjal （Solanum melongena）, for the treatment of various important dyes used in textile and other industries....In this study an effort has been made to use plant polyphenol oxidases; potato （Solanum tuberosum） and brinjal （Solanum melongena）, for the treatment of various important dyes used in textile and other industries. The ammonium sulphate fractionated enzyme preparations were used to treat a number of dyes under various experimental conditions. Majority of the treated dyes were maximally decolorized at pH 3.0. Some of the dyes were quickly decolorized whereas others were marginally decolorized. The initial first hour was sufficient for the maximum decolorization of dyes. The rate of decolorization was quite slow on long treatment of dyes. Enhancement in the dye decolorization was noticed on increasing the concentration of enzymes. The complex mixtures of dyes were treated with both preparations of polyphenol oxidases in the buffers of varying pH values. Potato polyphenol oxidase was significantly more effective in decolorizing the dyes to higher extent as compared to the enzyme obtained from brinjal polyphenol oxidase. Decolorization of dyes and their mixtures, followed by the formation of an insoluble precipitate, which could be easily removed simply by centrifugation.展开更多
基金funded by the Researchers Supporting Project Number(RSP2023R 509),King Saud University,Riyadh,Saudi Arabia.
文摘The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.
文摘In this study an effort has been made to use plant polyphenol oxidases; potato （Solanum tuberosum） and brinjal （Solanum melongena）, for the treatment of various important dyes used in textile and other industries. The ammonium sulphate fractionated enzyme preparations were used to treat a number of dyes under various experimental conditions. Majority of the treated dyes were maximally decolorized at pH 3.0. Some of the dyes were quickly decolorized whereas others were marginally decolorized. The initial first hour was sufficient for the maximum decolorization of dyes. The rate of decolorization was quite slow on long treatment of dyes. Enhancement in the dye decolorization was noticed on increasing the concentration of enzymes. The complex mixtures of dyes were treated with both preparations of polyphenol oxidases in the buffers of varying pH values. Potato polyphenol oxidase was significantly more effective in decolorizing the dyes to higher extent as compared to the enzyme obtained from brinjal polyphenol oxidase. Decolorization of dyes and their mixtures, followed by the formation of an insoluble precipitate, which could be easily removed simply by centrifugation.
