TY - GEN
T1 - Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes
AU - Wellu, Ziem Patrick
AU - Amissah, Daniel Kwame
AU - Wilson, Matilda Serwaa
AU - Appati, Justice Kwame
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This study introduces an advanced framework for plant disease detection, specifically classifying tomato images into “Early Blight” and “Healthy” categories. Utilizing a fusion of artificial intelligence and computer vision, the research employs the MobileNet architecture enriched with custom convolutional layers for enhanced feature extraction. The model's adaptability to different dataset sizes highlights its robustness, with performance benchmarks indicating up to 100% accuracy using classifiers like Random Forest, SVM, and Gradient Boosting. The framework further leverages ensemble classifiers to refine prediction accuracy, addressing the real-world complexities of variable lighting and environmental conditions. In its entirety, the research offers a scalable, accurate, and systematic approach to automated plant disease detection, with implications for bolstering global food security and sustainable agriculture.
AB - This study introduces an advanced framework for plant disease detection, specifically classifying tomato images into “Early Blight” and “Healthy” categories. Utilizing a fusion of artificial intelligence and computer vision, the research employs the MobileNet architecture enriched with custom convolutional layers for enhanced feature extraction. The model's adaptability to different dataset sizes highlights its robustness, with performance benchmarks indicating up to 100% accuracy using classifiers like Random Forest, SVM, and Gradient Boosting. The framework further leverages ensemble classifiers to refine prediction accuracy, addressing the real-world complexities of variable lighting and environmental conditions. In its entirety, the research offers a scalable, accurate, and systematic approach to automated plant disease detection, with implications for bolstering global food security and sustainable agriculture.
KW - Convolution
KW - Detection
KW - Early Blight
KW - Ensembles
KW - Food security
KW - MobileNet
UR - http://www.scopus.com/inward/record.url?scp=85199478308&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-2053-8_10
DO - 10.1007/978-981-97-2053-8_10
M3 - Conference contribution
AN - SCOPUS:85199478308
SN - 9789819720521
T3 - Lecture Notes in Networks and Systems
SP - 131
EP - 141
BT - Communication and Intelligent Systems - Proceedings of ICCIS 2023
A2 - Sharma, Harish
A2 - Shrivastava, Vivek
A2 - Tripathi, Ashish Kumar
A2 - Wang, Lipo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Communication and Intelligent Systems, ICCIS 2023
Y2 - 16 December 2023 through 17 December 2023
ER -
