Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management - Md Rakibul Hasan; Md. Mahbubur Rahman; Fahim Shahriar; Md. Saikat Islam Khan; Khandaker Mohammad Mohi Uddin; Md. Mosaddik Hasan (Crop Design)
Since the world's population is rising continuously, more cultivable land is being utilized for their dwellings. As a result, proper plan and technological breakthroughs shall be necessary to solve the food shortage. Tomato is a kind of vegetable which has the healthy ingredients and essential for our daily food supply. The proposed system suggests an IoT based tomato cultivation and pest management system, with the help of learning methods. In the IoT implementation, camera module and moisture sensor are used to collect images of tomato plant and soil condition, respectively. Based on the moisture content, the water pump will supply the water necessary for crop growth. Besides, the real-time images of tomato leaves will be sent to the server to identify and classify natural enemies like various insect species. In the proposed system seven types of pests are identified with the help of 10 learning models like InceptionV3, Xception, InceptionResNetV2, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, DenseNet121, DenseNet169, DenseNet201. This study has trained with leaves and insects separately to identify whether or not an image from a tomato plant is insectoid 458 images of pests and 912 images of leaves are utilized in the proposed architecture. The accuracy of classifying insects or leaves using DenseNet201 is 100 ​%. The highest accuracy of 94 ​% is obtained to classify the different insects using the DenseNet201 model.