Introduction of Ontologies and AI Systems + Best buy price
Ontologies are being used in systems to represent knowledge in various AI technologies and model different CPS ecosystems where smart farming has transformed the agricultural field which has helped create the co-op necessary for cooperative success
The system has helped farmers in making decisions during extreme weather events that are frequent and critical for agricultural sectors
Smart Farming has revolutionized agriculture, which in turn has helped increase the quantity and quality of food and raw materials
Establishing a legal and contractual entity in the early stages of a cooperative is necessary for the success of the cooperative because abuse of the cooperative’s services and common resources by its members may not achieve its goals and ultimately has led to disintegration
Autonomous mobile robots are also tools in precision agriculture to perform various tasks
Control robots can adapt and learn, which is critical for agriculture, which is a powerful strategy
Most autonomous robots have sensors that input data, which is then processed by a control unit
The robot control system can be based on fuzzy logic
Robots can be used to inspect and handle plants thanks to built-in touch and optical sensing systems
Some other widely used robotic applications are weed picking and robotic weed control, which are based on machine vision and include accurate chemical applications
This seems very practical as manual weed control is a very tedious and inefficient task that adds to human labour
In addition to this, robots have been used for phenotypic plant health assessment
While different robots use different navigation systems, they are usually guided by GPS and a human-controlled laptop as they move between rows of plants
Similarly, there has been progress in using robots to harvest crops such as apples, grapes and others
Well-established AI techniques, when applied to data collected from field sensors, can help develop more efficient data-driven intelligent farming systems
They used the described neural networks to describe plant stress severity based on visual models
The AI seeding app is developed by Microsoft
The system makes recommendations such as the optimum timing of seed sowing, soil preparation for cultivation, etc
Lee et al
developed a tool that helped identify the pest hazards of fruit trees
Tools like AutoTrac use AI techniques to evenly plant crops to reduce overlap and excessive plant spacing
Blue River Technology has used computer vision techniques to identify individual plants and identify anomalies
Ca et al
, detail a method for classifying dense grasslands from aerial photographs using a cascaded convolutional neural network encoder-decoder
Another AI approach showing promising results is the use of support machines for sorting and fuzzy logic to automatically sort agricultural products without human intervention
Diagnosis of diseases in plants Plants are very susceptible to disease as they are ecosystems, so disease prevention and control is very important
Current crop conditions and severity of infection affect the rate of spread of the disease
The key to preventing loss of crops and agricultural products is the detection of plant diseases
Colorful spots or stripes that can appear on the leaves, stems, and seeds of a plant are several signs that the plant is sick
Therefore, early diagnosis remains elusive in many parts of the world
Advances in computer vision through deep learning have paved the way for disease diagnosis using smartphones
The tedious task of observing huge crop fields and detecting disease symptoms at an early stage is very tedious, so automated systems are useful
Therefore, the aim was to use a software program based on image processing to detect and classify plant root diseases automatically
Patil and Kumar (2011) aimed to provide a variety of advanced methods for plant disease/trait research using image processing to increase productivity and reduce costs associated with bringing in specialists in plant disease diagnosis
Identification of diseased leaves, leaves, fruits, quantification of diseased area, morphology of affected area, color of affected area, determination of size and shape of fruits, etc
Image processing is useful
A manual evaluation script that changes the quality limiting step to imaging can be extended beyond its feasibility study by automating an image analysis experiment
Several methods and approaches can be used to classify and diagnose diseases using computer vision
Deep convolutional neural networks were used, achieving 99
53% success in diagnosis with the corresponding plant
Neural networks have also been used to detect diseases in crops such as rice
K-Means Algorithm, Principal Component Analysis (PCA), Coefficient of Variation (CV), Support Vector Machines (SVM) are also some of the other options and in context some model method is more efficient
In the illustrative study, K-means clustering classification into two groups: healthy and infected, followed by assistive machines (SVMs), produced better results than ANNs
All the visible characteristics of an organism resulting from the interaction of its species (complete genetic inheritance) with the environment can be described as phenotyping
Traits may include behavioral, chemical, color, shape, and size
Collection, analysis and application of plant statistics remain inadequate
Furthermore, the phenotype reflects an enormous number of processes, functions and structures that change during the course of growth and development
A prerequisite for breeding, introduction of varieties, genomic and phenomics studies is a thorough evaluation of crop varieties
Increasing the yield is the main objective and problem of plant breeding
Dee and French (2015) sought to propose an automated system based on computer vision that can perform feature identification and measurement without human intervention, in which we can obtain higher output and higher accuracy in less time and even at lower cost
Most cost traditional ones
function
According to Coppens et al
(2017) Robotic imaging systems can significantly increase the throughput volume of characterization, thus overcoming the perceived drawback of gene design
Thus, the efficiency of new phenotyping and genotyping methods should be evaluated considering the relative genetic gains that can be obtained through the introduction of new methods, whereas the cost-benefit should be evaluated in relation to cost and cost of additional genetic benefits
