| dc.description.abstract | Coconut farming faces significant threats from pest-related diseases, which are challeng ing to identify and manage manually due to the height of trees and the intricate structure
of their leaves. Traditional inspection methods, achieving accuracy rates of about 60-
80%, are labor-intensive and often unreliable. This systematic review investigates the
application of Artificial Intelligence (AI), particularly Convolutional Neural Networks
(CNNs), to enhance pest detection in coconut leaves. Following PRISMA guidelines, this
study analyzed 50 research articles published between 2010 and 2024, sourced from
ResearchGate and ScienceDirect. Among these, 22 studies focused on symptoms such as
leaflet damage, caterpillar infestations, yellowing, drying, and flaccidity. EfficientNetB7
was identified as a top-performing model, achieving an accuracy 93.72%, thereby
demonstrating substantial improvements in detection accuracy and potential for real world applications. Conversely, ResNet50 and VGG16 exhibited limited effectiveness
compared to more advanced architectures. Key Challenges include misclassification
due to symptom overlap, limited dataset diversity, and environmental variability. This
review emphasizes the importance of explainable AI, domain adaptation, and scalable
models to enhance pest detection systems. Future research should focus on developing
real-time diagnostic tools and integrating AI-driven approaches into sustainable pest
management practices to improve efficiency and optimize resource utilization. | en_US |
