Title: Identifying The Coconut Leaf Disease Using Deep Transfer Learning Approach
Abstract: Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the ’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry.
Keywords: coconut leaf disease;nutrient deficiency;pest attacks;VGG19;MobileNet v2;Inception v3;ResNet50
Review score: Moderate
Review comment: The methodology employed, utilizing neural networks to detect and differentiate between three specific coconut leaf diseases is robust and well-designed. The selection and comparative analysis of four different models (VGG19, MobileNet v2, Inception v3, and ResNet50) are well-executed, with the ResNet50 model demonstrating superior accuracy at 99.36% based on a dataset of 3166 images. This high level of accuracy is promising for the practical application of this technology in the field, potentially revolutionizing coconut agriculture by enabling farmers to take timely and effective measures against diseases.

The paper is well-structured, with a clear abstract that succinctly outlines the study's background, objectives, methodology, and significant findings. The focus on both economic and ecological impacts highlights the broader relevance of the study, not just for farmers but for the agricultural sector and environmental conservation efforts.

One area for improvement could be a more detailed discussion on the implementation of these neural network models in real-world settings. Information on how farmers can access and use this technology, any associated costs, and training or support mechanisms would enhance the practical applicability of the research findings.