Shape classification technology of pollinated tomato flowers for robotic implementation

Abstract

Three pollination methods are commonly used in the greenhouse cultivation of tomato. These are pollination using insects, artificial pollination (by manually vibrating flowers), and plant growth regulators. Insect pollination is the preferred natural technique. We propose a new pollination method, using flower classification technology with Artificial Intelligence (AI) administered by drones or robots. To pollinate tomato flowers, drones or robots must recognize and classify flowers that are ready to be pollinated. Therefore, we created an AI image classification system using a machine learning convolutional neural network (CNN). A challenge is to successfully classify flowers while the drone or robot is constantly moving. For example, when the plant is shaking due to wind or vibration caused by the drones or robots. The AI classifier was based on an image analysis algorithm for pollination flower shape. The experiment was performed in a tomato greenhouse and aimed for an accuracy rate of at least 70% for sufficient pollination. The most suitable flower shape was confirmed by the fruiting rate. Tomato fruit with the best shape were formed by this method. Although we targeted tomatoes, the AI image classification technology is adaptable for cultivating other species for a smart agricultural future.

Autonomous Drone-Based Pollination System Using AI Classifier to Replace Bees for Greenhouse Tomato Cultivation

“…Section II provides details of the AI classification technology for searching flowers [21], [22]. Accurate flight position location technology is required for a drone to fly autonomously.…”

Development, Implementation and Evaluation of An Epidemic Communication System

“…The proposed model of the epidemic communication system employs a network in which drones relay images of flowers, as shown in Figure 3. The background of this system model is an autonomous drone system for pollinating tomato flowers or pear flowers, where clustered drones fly around the stem of a tomato or the tree of a pear, searching for pollinatable flowers [13][14][15][16]. The drones are equipped with cameras, and by relaying the captured images of flowers to an external network, an external server performs artificial intelligence (AI) image classification.…”

“…It is assumed that image data transferred using these protocols will be received at the receiving station, after which the data will be transferred to a cloud server in the wired network. For example, the cloud server uses AI classification technology by analyzing image data to classify flowers that can be pollinated [13]. In this case, duplicate images can be discarded.…”

“…IoT 2024, 5 Therefore, the traditional framework of Internet communication does not consider scenarios where vehicles or individuals physically move to locations without an established wired or wireless infrastructure to transport information. Delay-tolerant networking (DTN) addresses the challenges posed by the constraints of traditional communication technologies in such scenarios [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Is the Artificial Pollination of Walnut Trees with Drones Able to Minimize the Presence of Xanthomonas arboricola pv. juglandis? A Review

“…These technologies include various methods for pollen collection and application, such as hand pollination, handheld and vehicle-mounted devices, unmanned aerial vehicles (UAVs), and robotic and autonomous pollinators. These systems have been found to perform adequately for certain crops, including kiwifruit, olive, date palm, walnut, and tomato [13,14].…”