Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield.The present study introduces a reliable deep learning platform called"Dee...Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield.The present study introduces a reliable deep learning platform called"Deep Learning-Crop Platform"(DL-CRoP)for the identification of some commercially grown plants and their nutrient requirements using leaf,stem,and root images using a convolutional neural network(CNN).It extracts intrinsic feature patterns through hierarchical mapping and provides remarkable outcomes in identification tasks.The DL-CRoP platform is trained on the plant image dataset,namely,Jammu University-Botany Image Database(JU-BID),available at https://github.com/urfanbutt.The findings demonstrate implementation of DL-CRoP-cases A(uses shoot images)and B(uses leaf images)for species identification for Solanum lycopersicum(tomato),Vigna radiata(Vigna),and Zea mays(maize),and cases C(uses leaf images)and D(uses root images)for diagnosis of nitrogen deficiency in maize.The platform achieved a higher rate of accuracy at 80-20,70-30,and 60-40 splits for all the case studies,compared with established algorithms such as random forest,K-nearest neighbor,support vector machine,AdaBoost,and naive Bayes.It provides a higher accuracy rate in classification parameters like recall,precision,and F1 score for cases A(90.45%),B(100%),and C(93.21),while a medium-level accuracy of 68.54%for case D.To further improve the accuracy of the platform in case study C,the CNN was modified including a multi-head attention(MHA)block.It resulted in the enhancement of the accuracy of classifying the nitrogen deficiency above 95%.The platform could play an important role in evaluating the health status of crop plants along with a role in precise identification of species.It may be used as a better module for precision crop cultivation under limited nutrient conditions.展开更多
基金funded by a CSIR-SRF fellowship to M.U.and the Department of Science and Technology-Science and Engineering Research Board(DST-SERB)Core Research Grant(CRG)to S.P.C.
文摘Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield.The present study introduces a reliable deep learning platform called"Deep Learning-Crop Platform"(DL-CRoP)for the identification of some commercially grown plants and their nutrient requirements using leaf,stem,and root images using a convolutional neural network(CNN).It extracts intrinsic feature patterns through hierarchical mapping and provides remarkable outcomes in identification tasks.The DL-CRoP platform is trained on the plant image dataset,namely,Jammu University-Botany Image Database(JU-BID),available at https://github.com/urfanbutt.The findings demonstrate implementation of DL-CRoP-cases A(uses shoot images)and B(uses leaf images)for species identification for Solanum lycopersicum(tomato),Vigna radiata(Vigna),and Zea mays(maize),and cases C(uses leaf images)and D(uses root images)for diagnosis of nitrogen deficiency in maize.The platform achieved a higher rate of accuracy at 80-20,70-30,and 60-40 splits for all the case studies,compared with established algorithms such as random forest,K-nearest neighbor,support vector machine,AdaBoost,and naive Bayes.It provides a higher accuracy rate in classification parameters like recall,precision,and F1 score for cases A(90.45%),B(100%),and C(93.21),while a medium-level accuracy of 68.54%for case D.To further improve the accuracy of the platform in case study C,the CNN was modified including a multi-head attention(MHA)block.It resulted in the enhancement of the accuracy of classifying the nitrogen deficiency above 95%.The platform could play an important role in evaluating the health status of crop plants along with a role in precise identification of species.It may be used as a better module for precision crop cultivation under limited nutrient conditions.
