Anurag Patel, Assistant Professor
A.B. Singh, HOD,
School of Agriculture, Sanjeev Agrawal Global Educational University, 
Bhopal, Madhya Pradesh, India

Abstract
The agricultural production system has undergone significant changes in recent decades due to advancements in robotics and artificial intelligence (AI). A labor shortage during critical crop seasons has highlighted the need for alternative solutions that advocate for safe and sustainable farming practices. Key technologies, including the Internet of Things (IoT), machine learning, and robotics, have become vital for performing essential agricultural tasks. Enhanced electronics and computing applications now support robotic systems in various field operations, such as transplanting, harvesting, and intercultural operations for both agricultural and horticultural crops. Integration with vision-based systems and global positioning systems (GPS) allows for higher precision in these operations. Notably, a robotic transplanter designed for installing plug-type seedlings utilizes a robotic arm, manipulator, and end-effector, driven by computer vision and motion planning algorithms. Furthermore, robotics can enhance crop management through movement, localization, and targeted interventions via drones, meeting specific spatial and temporal crop management demands. Its applications include spraying, weeding, and harvesting fruits. Despite its potential, robotic technology in agriculture remains at an early developmental stage, driven by increasing labor shortages, rising wage demands, and the urgency for timely field operations. Although numerous research initiatives have examined robotics in agriculture, continued research is crucial to innovate next-generation robots capable of managing demanding and labor-intensive farming tasks.

Introduction
Agriculture remains a crucial sector for our country's survival, traditionally relying heavily on labor-intensive practices and equipment. To enhance sustainability and protect the welfare of farmers and agricultural laborers, the sector must adopt modern farming technologies, such as agricultural robotics. Various robotic applications are currently in development for tasks like transplanting, weeding, spraying, and harvesting. While developed nations have seen advancements in agri-robot technology, similar initiatives are emerging in developing countries, including India, where efforts are being made in areas like crop mapping and disease identification utilizing vision-based systems. Drones have also become popular for monitoring crop health and performing spraying operations. Although many of these technologies are still in the prototype phase, their research and development are critical for integration into real-world farming practices, ensuring sustainable production with less reliance on manual labor. This article focuses on available robotic systems for a range of agricultural operations, offering insights into their potential applications in both field crops and horticulture.

Seeding and Transplanting
Manual vegetable transplanting of plug seedlings is the most time intensive and laborious work. Robotic transplanter may be a solution for the operation it not only saves time but also requires less labour. The robotic transplanters use computer graphics or machine vision system for simulating transplanting operation. They consist of a robotic arm for seedling pick-up, a path manipulator and an end-effector. An intelligent transplanting system consists of 5 bar picking mechanism with fixed gear train, the seedling tray conveying mechanism, the planting mechanism, the seedling detection system using PLC. The PLC is used to control transmission and detect the void cell of seedling tray. By using robotic transplanter for transplanting seedlings of vegetables and crops, it will assure precision with safe and comfortable operation.

Robotic seeding

Intercultural
Intercultural operation such as weeding is done to kill the weeds by mechanical weeders or chemical spraying. Manual hand weeding in field crop is considered as the most drudgerious farm operation and demands huge manual labour. Weeding by herbicides not only involves high input costs but also degrade the environment and hence the overall productivity. Robotic weeding may offer a potential alternative for conventional weeding practices using hand tools. Also, due to the strict protocols and restriction on the use of herbicides, robotic weeding offers the best alternative to manual weeding. Robotic weeder uses vision-based systems for weed detection, guiding weeder and uprooting weeds mechanically. Co-robots developed by US National Science Foundation can work as human partner as a co-worker to perform a task jointly with ease.

Robotic weeder

Spraying
During spraying of agro-chemicals, contamination is the major problem which may cause threat to human health, if proper protection is not taken. Robotic sprayers are being developed and extensively used in orchards like apples, grapevine, cherries, etc. and to some extent in greenhouses. These sprayers are developed for target oriented application and to enhance input use efficiency. “In current farming practice, pesticides are typically applied uniformly across fields, despite many pests and diseases exhibiting uneven spatial distributions and evolving around discrete foci”. The amount of agrochemicals used in precision horticulture can be reduced by effective site-specific application of pesticide. The recently developed automatic variable-rate sprayer requires accuracy in measurement of location, canopy size and application of adequate amount of agro-chemicals to reduce environmental losses and save inputs. An autonomous system can also reduce labour requirement which can be employed for other activities, hence increases the crop yield, agricultural profitability and economic survival.

Robotic sprayers

Harvesting
Fruit selection as well as detachment is one of the essential tasks for efficient harvesting. Most of the robotic harvester have been developed for fruits like apple, citrus, cherries, strawberries, etc. However, some harvesters for crops grown in greenhouse such as tomatoes, capsicum, etc. were also developed. The harvesting of fruits is accomplished by grasping the fruit with grippers and then detaching it on the basis of shape, size, colour and texture.

Robotic fruits harvester

Aerial Robot or Drones
Aerial Robots, commonly known as Drones or Unmanned Aerial Vehicles (UAVs), are increasingly used in agriculture for various operations such as crop mapping, scouting, and spraying, especially during labor shortages. UAVs offer significant advantages over traditional satellite imagery, including higher pixel resolution, independence from cloud cover during critical growth periods, and immediate information transmission. They are instrumental in vineyard management, mapping grass species, measuring shrub biomass, and assessing crop health. Additionally, UAVs assist in weed detection, crop water stress monitoring, and evaluating nitrogen treatments. Research indicates that drone usage can reduce production costs by 25-30% through effective pest detection and timely spraying, making them a valuable tool for farmers.

Unmanned ground vehicle (UGV)
Unmanned ground vehicles (UGVs) in agriculture face significant challenges navigating unstructured terrain, which can compromise their stability and effectiveness. Unlike human operators who can dynamically manage such interactions, UGVs must rely on advanced technologies for positioning, navigation, and sensing to overcome these uncertainties. The integration of Global Navigation Satellite Systems (GNSS) has enhanced the functionality of autonomous vehicles, allowing for precise steering and guidance in agricultural applications. However, current systems do not provide full autonomy, necessitating additional safety measures for obstacle detection and communication with operators through wireless technologies, including cyber-physical systems (CPSs) and Internet of Things (IoT) frameworks. This connectivity is key for implementing decision-making processes informed by big data, with potential applications extending into machine learning and artificial intelligence. As smart agricultural systems evolve, UGVs must adopt principles from smart factories to enhance efficiency and reduce deployment delays. Available technologies can significantly benefit precision agriculture through coordination among UGVs, detection devices, and farm management systems. Two notable trends in UGV development are the automation of traditional agricultural vehicles and the creation of specialized mobile platforms, both of which are currently the focus of numerous research initiatives and companies aiming to improve agricultural practices.

Unmanned tractor Unmanned surveyor

Future prospects
Robots in agriculture, initially seen as impractical, are becoming pivotal for ensuring food security amid a growing population and labor shortages due to workforce migration. The demand for labor-intensive tasks such as transplanting, weeding, and harvesting can be met by robotic machinery, which can also mitigate the limitations posed by labor scarcity in these areas. Drones are also emerging as valuable tools for spatial and temporal field crop management, aiding large-scale crop planning and monitoring. While digital solutions like mobile apps exist, the adoption of robotic technology remains in its early stages. Technological developments include driverless tractors and specialized harvesters for greenhouse crops. However, challenges such as climatic variations, high costs, inefficiencies, and the need for skilled operators hinder widespread adoption. Concurrently, certain robotics applications for fruit harvesting and sprayers are commercially available. Future implementation of autonomous robots necessitates collaborative human-robot systems and could benefit from multi-stakeholder initiatives to accelerate their integration into the agricultural sector, addressing anticipated labor shortages effectively.

Conclusion
The problems like labour shortages and growing wage costs, robotics and artificial intelligence have the potential to revolutionise agriculture. Tasks like seeding, crop monitoring, and harvesting are being improved by key technologies like machine vision, IoT, GPS/GNSS, machine learning, UAVs, and unmanned ground vehicles. While encouraging sustainability, innovations like autonomous sprayers and robotic transplanters increase precision and resource efficiency. Drones and UGVs are supporting the shift towards data-driven farming, even though many solutions are still being developed. For wider adoption, issues like high initial costs and system dependability must be resolved, particularly in developing nations. Future success, which has the potential to greatly increase agricultural productivity and contribute to global food security, will depend on human-robot cooperation, AI developments, and supportive policies.