King Saud University

Ghulam Muhammad

Mohamed Amine Mekhtiche

Mohamed Abdelkader Bencherif

Mohammed Mahdi Algabri

Mansour Mohammed A Alsulaiman

Hamid Abdulsalam Ghaleb

Mohammed Faisal Abdulqader Naji

Hamdi Taher Altaheri

Hassan Ismail H Mathkour

Abdul Wadood

Khaled Soliman Alghathbar

Hebah Abdulaziz Elgibreen

Taha Mohammed Ahmed Alfaqih

Khalid Nasser Almutib

Muhannad Funaysan Alanazi

Muneer Hamid Ahmed Al-Hammadi

Hedjar Tahar Ramdane

Waleed Mohammad Albaqami

Nasser Khalid Alzamil

Amin Umar Syed

Mansour Mohammed Alsulaiman

Fadl Dahan Naji

Yousef Ahmed M Alohali

A system for detecting diseases among plants includes a mobile device such as a smartphone, table, or personal computer, and a mobile robot which sends photographs of diseased plants to the mobile device including a plant name, disease type and location of the plant. The mobile robot includes a power supply, housing unit, camera, navigation unit, GPS unit, wireless transmitter, and traction unit for mobility of the robot. A geared motor drives the traction unit, and is connected to a low-level controller for motion control. A high-level controller is connected to the camera, navigation unit, GPS unit, and programmed with a convolutional neural network (CNN) configured to process images taken by the camera and associate with each image a plant type, disease type, and location of where the image was taken. The CNN may be a you only look once (YOLO) model trained with images from a dataset.

System and method for detecting diseases among plants

The robotic system for harvesting, and performing pre- and post-harvesting tasks of date palm trees includes three main parts: a truck platform, a scissor lift, and a multi-task system. The truck platform provides mobility for the whole system, while the scissor lift raises the multi-task system to the top of the palm tree. The multi-task system is fixed on top of the scissor lift and includes a large basin that catches fruits during harvesting. A circular track is fixed within the basin and includes a 2-degree-of-freedom (DOF) slider base mounted thereon. On top of the 2-DOF slider base is a robotic arm fitted with the proper tool (harvesting tool, maintenance tool or bagging system) for the task to be performed. The circular track has pivoting sections, and the basin has a flexible portion in the rear of the truck platform to allow the system to surround the palm tree.

Robotic system for harvesting and maintaining date palms

The tree harvesting tool is a tool adapted for attachment to a conventional lifter or lifting device for performing harvesting and pre-harvesting operations on a fruit tree. The tree harvesting tool includes a cylindrical shell, having an upper end and a lower end, at least one portion of the cylindrical shell defining a door. The cylindrical shell is adapted for encircling the trunk of the tree. A plurality of panels are pivotally secured to the lower end of the cylindrical shell to define an openable floor having a central opening for receiving the trunk of the tree. A circular track is mounted on the upper end of the cylindrical shell, such that a movable platform may be mounted thereon. A robotic arm is mounted on the movable platform for selectively operating and manipulating a tool for performing tree harvesting and pre-harvesting operations.

Tree harvesting tool

The present disclosure introduces a new technique for environmental recognition of audio input using feature selection. In one embodiment, audio data may be identified using feature selection. A plurality of audio descriptors may be ranked by calculating a Fisher's discriminant ratio for each audio descriptor. Next, a configurable number of highest ranking audio descriptors based on the Fisher's discriminant ratio of each audio descriptor are selected to obtain a selected feature set. The selected feature set is then applied to audio data. Other embodiments are also described.

Environment recognition of audio input

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Riyadh, Saudi Arabia

Ghulam Muhammad