Early Detection of Coconut Palm Leaflet Diseases using Convolutional Neural Networks based Deep Learning Approach

In tropical regions, coconut palms are an essential part of the economy and daily life, providing food, oil, and many other products. However, their health and productivity can be affected by various diseases. Early and precise disease identification is essential for the management and prevention of many diseases. Leaflet diseases in coconut plants are those that impact individual leaflets, or leaf segments, on the fronds of coconut palms. Drying leaflets in coconut tree leaves can result from a variety of factors, including biotic (living organisms) and abiotic (environmental or non-living) impacts. In coconut trees, chlorosis, or the yellowing of the leaves, is a prevalent issue that can be caused by some factors. The CNN model is trained on a sizable set of high-resolution images of both healthy and diseased coconut leaves. Each disease's unique characteristics and patterns are automatically acquired with the aid of the deep learning architecture, enabling accurate categorization. Using an additional set of annotated photos of coconut tree leaves, the CNN model is assessed with remarkable accuracy in identifying illnesses. Comparing the system to traditional manual diagnosis procedures demonstrates the technology's efficacy and potential for early disease identification. Using this strategy raises the algorithm's accuracy to 95%