Shruti. R. Danve

Bio: Shruti. R. Danve is an academic researcher. The author has contributed to research in topics: Precision agriculture. The author has an hindex of 1, co-authored 1 publications receiving 28 citations.

Precision agriculture robot for seeding function

Abstract: In recent years, robotics in agriculture sector with its implementation based on precision agriculture concept is the newly emerging technology. The main reason behind automation of farming processes are saving the time and energy required for performing repetitive farming tasks and increasing the productivity of yield by treating every crop individually using precision farming concept. Designing of such robots is modeled based on particular approach and certain considerations of agriculture environment in which it is going to work. These considerations and different approaches are discussed in this paper. Also, prototype of an autonomous Agriculture Robot is presented which is specifically designed for seed sowing task only. It is a four wheeled vehicle which is controlled by LPC2148 microcontroller. Its working is based on the precision agriculture which enables efficient seed sowing at optimal depth and at optimal distances between crops and their rows, specific for each crop type.

iPathology: Robotic Applications and Management of Plants and Plant Diseases

Abstract: The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.

Solar Powered Autonomous Multipurpose Agricultural Robot Using Bluetooth/Android App

Abstract: In India nearly about 70 percentage of people are depending on agriculture. Numerous operations are performed in the agricultural field like seed sowing, grass cutting, ploughing etc. The present methods of seed sowing, pesticide spraying and grass cutting are difficult. The equipment's used for above actions are expensive and inconvenient to handle. So the agricultural system in India should be encouraged by developing a system which will reduce the man power and time. This work aims to design, develop and design of the robot which can sow the seeds, cut the grass and spray the pesticides, this whole system is powered by solar energy. The designed robot gets energy from solar panel and is operated using Bluetooth/Android App which sends the signals to the robot for required mechanisms and movement of the robot. This increases the efficiency of seed sowing, pesticide spraying and grass cutting and also reduces the problem encountered in manual planting.

Robotics and Automation in Agriculture: Present and Future Applications

Abstract: Agriculture is the backbone of society as it mainly functions to provide food, feed and fiber on which all human depends to live. Precision agriculture is implemented with a goal to apply sufficient treatments at the right place in the right time with the purpose to provide low-input, high efficiency and sustainable agricultural production. In precision agriculture, automation and robotics have become one of the main frameworks which focusing on minimizing environmental impact and simultaneously maximizing agricultural produce. The application of automation and robotics in precision agriculture is essentially implemented for precise farm management by using modern technologies. In the past decades, a significant amount of research has focused on the applications of mobile robot for agricultural operations such as planting, inspection, spraying and harvesting. This paper reviews the recent applications of automation and robotics in agriculture in the past five years. In this paper, the recent implementations are divided into four categories which indicates different operations executed for planting management starting from a seed until the product is ready to be harvested. Towards the end of this paper, several challenges and suggestions are described to indicate the opportunities and improvements that can be made in designing an efficient autonomous and robotics system for agricultural applications. Based on the conducted review, different operations have different challenges thus require diverse solutions to solve the specific operational problem. Therefore, the development process of an efficient autonomous agricultural robotic system must consider all possibilities and challenges in different types of agricultural operation to minimize system errors during future implementation. In addition, the development cost needs to be fully considered to ensure that the farmers will be able to invest their capital as a consumer. Therefore, it will become highly possible for the autonomous agricultural robotic system to be widely implemented throughout the world in the future.

Autonomous Seed Sowing Agricultural Robot

Abstract: In agriculture there is need for a technology that is more easily understood, implemented and used by the farmers. Equipment that requires less human effort and time with less cost of implementation is much required for success in agricultural industry. Autonomous robots built with less maintainance and that are portable as well as customizable according to the requirements might serve the purpose here and thus this paper presents you the design of a four wheel drive robot that does the work of seed sowing in ploughed agricultural land avoiding the human effort by tracing the path and sowing seeds at equal intervals using the field area parameters(length and breadth) and seed spacing intervals as inputs specified by the user. It also takes you through the design process of the robot, explains in detail the protype and the equipment used for building it and the difficulties faced during the different phases of the project. The paper also presents the sequence of steps to be followed to come up with designing a wheel drive robot and the parameters that has to be taken into consideration before building the prototype.

Machine Vision and Machine Learning for Intelligent Agrobots: A review

Abstract: An intelligent precise autonomous farming by an agricultural robot achieves the farm duties possibly harvesting, weed detection, disease identification, pruning and fertilizing deals with path planning and mapping of the unstructured and uncertain environment. A machine vision-based Agrobots along with artificial intelligence provides unmanned ground vehicle and unmanned aerial vehicle to navigate the path and to implement the agricultural task for minimizing labour and increasing quality food production. The perception-related work uses a machine learning algorithm to detect the feature and analyze the agricultural tasks for the autonomous machine. The trained data sets create the ability for robots to learn and decide the farm practices. The dawn of autonomous system design gives us the outlook to develop a wide range of flexible agronomic equipment based on multi-robot, smart machines and human-robot systems which lessen waste, progresses economic feasibility also reduces conservational impact and intensifies food sustainability. The multi-tasking Agrobots overcomes the effort of farmers in agricultural husbandry, independent of the climatic conditions. In this paper, a study on Agrobots effective in a diverse environment, its control and action process conjoined with mapping and detection using machine vision and machine learning algorithms are distinguished.