The agricultural industry is growing fast with highly intelligible technologies due to the involvement of widely specialized professionals and investors with sufficient capacity to mitigate gaps experienced in the farming process. Of course, the growth of these technologies will be boundless including highly advanced automated and robotic machines for the entire production scale of a product.
Currently, global agriculture is in dire need of an increased production yield due to the gradual but exponential increase of the world’s population. This is evident in the United Nations population forecast that the world’s population will surpass its about 7 billion people now to over 9 billion by 2050 and as a result, if farmers continue with their existing semi-automated methods they will be under pressure to meet up.
Its no doubt that robotic devices are improving farm yields and making the farming experience exciting in diverse ways depending on the kind of crop and methodology involved. These intelligent machines can be designed as self-controlled tractors, fly-bots, manipulators, or arm but based on their application.
Farm Weed Control
This is one of the poorly mechanized aspects of farming with too many manual ways of handling weeds amongst crops. These manual ways have a lot of effect on the farm productivity, for example, the use of herbicides can affect the growth of edible crops even though the weeds end up been taken out, and using hand picking method is very drudgery and stressful. But effecting the application of machine vision, deep learning, and other integrated algorithms increases productivity, operability, remote interfacing, and intelligence for multi-stage farming procedures and so improves the farming capacity. Cognitive, deep learning and machine vision are emerging tools that utilize mapping, detection, and identification and weed patterns for target recognition. More so, effective weed control technology can play key roles hereby been robust and adaptable. It is robust if it controls weed regardless of field condition variation and it is adaptable if it can change its technique amid varying genetics, climate changes, and geometric weed population.
This is a phenomenon applied when the physical attributes of a plant are to be monitored for a sign of health defects. Due to how stressful this stage of agricultural production is, engaging the use of a robotic device can help achieve a lot especially on a large production field in a short time-scale. The robotic device is designed with sensors such as hyperspectral and temperature cameras as well as GPS scanners for location and weather monitoring. They are also used to retrieve some details of the plant – stem thickness, plant height, leaf wideness, and other features like weather condition and moisture content. When these data have been cumulated, they are stored for further analysis of modeling the plant to predict its future growth pattern.
These utilize various mechanical structures depending on the kind of crop to be harvested – root or surface crop. Although some other robotic devices are designed to harvest multiple types of crops just like it can when humans do similar tasks. When placed in position, the robot applies the use of artificial intelligence and various sensors to determine the ripeness and position of the product and with a gripper tool attached to it, it picks the crop and moves to the next plant. It can be engaged for 24 hours with its 3D environmental monitoring and smart motion sensing. This computer vision characteristic enables it to calculate the best pathway through the farm field and navigates once to grapple a crop.
Sorting and Packing
Automated sorting and packing can help reduce food loss and improve time efficiency, this eventually sums up to increase the value which is the desire of every farmer. Integrating computer vision on an automated conveyor line can help control and guide how the products are selected and segmented into different categories. Pre-determined size ranges are defined already in the system so that the vision camera identifies and aligns the produce to its proper position.
They assist in precision agriculture due to real-time observation, measurement, and implementation from derived data. These drones are integrated with sensors and vision cameras to carry out aerial imagery for 3D mapping of plant-soil to check for the level of soil nutrients and from it, further, analysis can help determine effective soil management. More so, they can be used for seed planting by adopting AI technology, it identifies the field rows to place the seeds in a defined manner. Other applications are spraying of fertilizers or pesticides to maintain high yield and to monitor plant health throughout the plant cycle. The drone devices are embedded with GPS that helps them locate any crop area due to mapping.
When integrated with robots, they are capable of effectively and distinctively identifying and locating subjects. This is possible due to the technological advancements in 3D remodeling, positioning, visual recognition, and fault tolerance. For diverse and uncoordinated characteristics of fruit, these new developments are aided by associated algorithms to do a 3D remodeling of the fruit to evaluate its spatial coordinates by stereo matching. This forms part of the sequence for harvesting in terrain deformed environments – visual sensors set up stereoscopic identification and localization algorithm then some strategies are utilized to emphasize how stereo vision identifies and pinpoints under diverse environmental situations which now leads to a review of the 3D remodel algorithm.
In conclusion, groundbreaking advancements have been made currently in robotics for agricultural tasks are increasing the value of farm produce and making high yield possible. Various researches are going on globally to give solutions to other areas of agriculture that are yet to be taken care of like most of these technologies are centered on the harvesting of crops. More so, there is a need to develop the machines to be able to handle soft crops for harvesting, sorting, and packing.
The use of agricultural robots in weed management and control (core.ac.uk)