YOLO (You Only Look Once) is an algorithm that uses Neural Networks to provide real-time object detection in images or video frames. This algorithm is popular because of its speed and accuracy.

This idea is going to use YOLOv8 which is the latest model of YOLO for detecting crop diseases in Oman. YOLO can provide farmers in Oman with a powerful tool to monitor and manage their crops, helping to increase efficiency, reduce waste, and improve the overall yield and quality of their products. Also, it can monitor crops and detect changes in plant growth, health, and quality. For example, YOLO can detect the presence of weeds or disease in crops, allowing farmers to take action before the problem spreads.  In Oman, crops such as dates, citrus fruits, and vegetables are grown. Common plant diseases in Oman include powdery mildew, downy mildew, black rot, and citrus canker. These diseases can cause significant damage to crops and reduce yields.Therefore, the traditional way for detecting crop diseases in Oman typically which involves visual inspection of the plants by farmers or agricultural experts will not be as fast as YOLO. By using computer vision algorithms like YOLO, farmers and researchers can detect these diseases early on and take appropriate measures to control or treat them. This can help prevent the spread of the disease and minimize crop losses. Moreover, they can reduce the use of pesticides and other harmful chemicals. The Treatment of the detected diseases using YOLO will be provided in our created Website.

Crop diseases can cause significant economic losses for farmers, especially in developing countries where agriculture is a major source of income. Globally, losses due to crop diseases can range from 5% to 80% of the crop yield, depending on the crop and disease severity. In Oman, crop diseases have caused significant losses in crops such as date palms, bananas, and citrus fruits. One of the most damaging crop diseases in Oman is the Fusarium wilt disease, which affects banana plants and can cause up to 80% yield losses. Moreover, The palm weevil (Rhynchophorus ferrugineus) is a serious pest of date palms in Oman and throughout the Middle East. According to a study published in the journal Insects in 2018, the economic impact of palm weevil damage in Oman was estimated to be around US$82 million annually. This figure includes both direct losses due to reduced yield and quality of dates, as well as indirect losses from the cost of control measures such as insecticides and pheromone traps. The study also estimated that the cost of controlling palm weevil infestations in Oman was approximately US$3.5 million per year. This includes the cost of insecticides, pheromone traps, and other control measures.

Multiple steps will be used to implement and disseminate this idea.

1- Roboflow Platform: which uses AI (Artificial Intelligence). We will use it to:

Step 1: Collect and annotate a set of images that contain examples of crop diseases we want to detect. We can then use Roboflow to annotate these images by drawing bounding boxes around the areas where the crop disease is present.

Step 2: Upload our data to Roboflow: Once our images are annotated, wecan upload them to Roboflow. Roboflow supports a variety of formats, including COCO, Pascal VOC, and YOLO.

Step 3: Preprocess your data: This includes resizing images, augmenting our dataset with additional images, and splitting our dataset into training and validation sets.

Step 4: Deploying the model to a Website that we create and can use it to detect crop diseases in real-world scenarios.

2- YOLO Installation and Training using Google Collab:

Step 1: we train YOLOv8 model to detect the crop diseases.

Step 2: we test the model, so we make sure it can find all the diseases.

3-Visual Studio for Creating Interactive Website: Design a user-friendly interface, so the user can interact with the model easily. The users can be Researchers, Experts, Farmers, interested parties and Ministry of Agriculture. In this interface we will add:

1-login page

2- About page (project Introduction, Team member information)

3-Upload Image/Video Page

4- Real-time Crop Diseases Detection page

5-Treatment Page

6-Contact page

Our main focus is the Real-time Crop Diseases Detection Page and the Upload Image/Video Page. The user can either use the model in Real-time or he/she can easily upload the wanted images or videos. In Real-time he/she will need to link their gadget (phone, camera etc..) to the same device that has the application on and start moving the gadget around the crops meanwhile the disease will appear on the application. Another way is, uploading the images after taking a clear picture of the disease to the application and run the process of the detection.

There are several ways to monetize this idea:

1- Sell access to the service: We can charge a fee to farmers, agricultural companies, or other interested parties to access the crop disease detection service on our website. This can be done through a subscription-based model or a pay-per-use model. For example, the first 5 uploaded images will be for free and uploading more than 5 images will be paid. Also, uploading video will be paid more than the images. In addition to that, the Real-Time Detection will be used by a pay-per-use model.

2- Affiliate marketing: We can promote related products or services on our website, such as crop treatments or agricultural equipment, and earn a commission for each sale made.

3- Advertisements: We can display ads on our website and earn revenue based on clicks or impressions.

4- Sponsorships: We can seek sponsorship from companies or organizations that are interested in promoting crop health and sustainability. This can involve displaying their logo on our website, promoting their products or services, or collaborating on research or outreach initiatives.

1- Identifying local crop diseases. 2- Monitoring disease outbreaks. 3- Reducing dependence on pesticides. 4- Improving crop yields by identifying and addressing crop diseases early.

1- Early Detection. 2- Accuracy. 3- Speed of Detection. 4- Reduced use of Pesticides. 5- Improved Crop Quality. 6- Improved Food Security.