TY - JOUR
T1 - JutePestDetect
T2 - An intelligent approach for jute pest identification using fine-tuned transfer learning
AU - Talukder, Md Simul Hasan
AU - Chowdhury, Mohammad Raziuddin
AU - Sourav, Md Sakib Ullah
AU - Rakin, Abdullah Al
AU - Shuvo, Shabbir Ahmed
AU - Sulaiman, Rejwan Bin
AU - Nipun, Musarrat Saberin
AU - Islam, Muntarin
AU - Islam, Mst Rumpa
AU - Islam, Md Aminul
AU - Haque, Zubaer
N1 - Publisher Copyright:
© 2023
PY - 2023
Y1 - 2023
N2 - In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models' performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide.
AB - In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models' performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide.
KW - Augmentation
KW - Densenet201
KW - Inceptionv3
KW - Jutepestdetect
KW - Mobilenetv2
KW - ResNet50
KW - Transfer learning
KW - VGG19
U2 - 10.1016/j.atech.2023.100279
DO - 10.1016/j.atech.2023.100279
M3 - Journal article
AN - SCOPUS:85166361044
SN - 2772-3755
VL - 5
JO - Smart Agricultural Technology
JF - Smart Agricultural Technology
M1 - 100279
ER -