EXPAND PRODUCTION AND ESTABLISH
ACCEPTABLE FILMS IN THE SRI LANKAN FILM INDUSTRY

The literature review discusses several critical aspects of the Sri Lankan film industry, focusing on mistake identification, character identification, feedback analysis, and film categorization. A mistake identification system leverages computer vision and neural networks to identify anachronistic elements like vehicles in historical films, ensuring accuracy and maintaining immersion. Character identification highlights the evolution of Sri Lankan cinema, showcasing how filmmakers reflect complex societal changes, gender dynamics, and identity through nuanced characters.

Feedback analysis emphasizes the importance of audience engagement beyond box office success, noting that modern tools like social media and influencer reviews play an increasing role in shaping public perception of films. Lastly, film categorization in Sri Lanka has shifted from traditional methods to more data-driven approaches, incorporating machine learning to predict audience preferences and optimize marketing strategies. The review also touches on the underrepresentation of horror in Sri Lankan cinema, despite growing interest in subgenres like zombies, and discusses the rise of documentary filmmaking as a vital genre for exploring real-world issues. Overall, technology and evolving cinematic trends are reshaping how Sri Lankan films are created, marketed, and received by audiences.

Proposed Problem

How to classify pest and diseases in coconut and provide surveillance to people in real time?

Weligama Coconut Leaf Wilt Disease (WCLWD) and Coconut Caterpillar Infestation (CCI) are the most threatening disease and pest of coconut in Sri Lanka. Due to WCLWD about 300,000 palms were removed and an estimated 60,000 palms needed to be identified for removal. An economical yield loss will result if more than 30% defoliation is caused due to CCI. For efficient management of WCLWD and CCI, identification at an early stage and effective communication of growers and professionals is needed.

The proposed pest and disease Surveillance system consists of 4 main components. They are;

1. WCLWD and its symptom severity identification
2. CCI Identification and progression level determination
3. Deficiency Identification (Mg deficiency and Leaf Scorch Decline)
4. Water resource identification
5. Crowdsourcing for information sharing.

Fig 4 illustrates the overall system diagram of the proposed solution which was intended to provide a smart approach for stakeholders, researchers, and Coconut Development Officers (CDOs) to detect the coconut diseases and pest infestations that may affect the coconut industry. As shown in the diagram, the registered users of the system can capture or upload the images which are suspicious. The images are sent to the Amazon Web Services (AWS) backend server where the flask server is deployed. These images are processed in the flask server by the designed DCNN models for disease identification. If WCLWD is found, the symptom severity will be determined using CNN models. Simultaneously, if CCI is identified, the images will go through the Mask-R-CNN model to determine the progression level while the number of caterpillars are extracted using the YOLOv5 object detection algorithm. Images will be classified using the CNN models of Mg deficiency and LSD at the same time.

Once the system identifies that the leaves are infected, then the response will be captured by the crowdsourcing platform. The Google Map will be updated with the real time locations (latitude and longitude) of the infected palms. In Addition, the system will automatically send notifications to the farmers and other stakeholders who are at the risk of infection.