Author and year

Description

Country

[15]

Reviews recent developments in image analysis systems for the plant growth and health evaluation

 

[16]

Realized the design of an expert system based on computer vision for real-time crop rows and weeds identification in maize fields

Spain

[17]

Proposed testing and validating the accuracy of four image processing algorithms (wavelet transforms and Gabor filtering) for crop/weed discrimination in synthetic and real images.

 

[18]

Studied a High-resolution images from digital cameras support of plant characteristics.

 

 

[19]

Present a system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem’s mistakes

 

Spain

[20]

Developed several computer-vision-based methods for the estimation of percentages of weed, crop and soil present in an image showing a region of interest of the crop field.

Spain

[21]

Used a method to provide real-time positional information of crop plants for a mechanical intra-row weeding robot.

China

[22]

Pproposed an automatic crop disease recognition method , which combined the statistical features of leaf images and meteorological data

China

[23]

Present a machine vision system for the identification of the visual symptoms of plant diseases, from coloured images.

UK

[24]

Present image-processing based method that identifies the visual symptoms of plant diseases, from an analysis of coloured images.

UK

[25]

Present an Autonomous spraying in vineyards and four machine vision algorithms that facilitate selective spraying.

Israel

[26]

Developing an easy and proficient automatic method for finding nitrogen and chlorophyll content in a plant based on leaf color and image processing

India

[27]

Design avision guidance system for automated weed detection robot

Pakistan

[28]

detect and recognize the plant stress caused by disease in the field conditions by combining hyperspectral reflection information between 450 and 900nm and fluorescence imaging.

Belgium

[29]

Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features

India

[30]

Development of a pattern recognition system that recognizes weeds and gives the occupation porcentage of wide and narrow leaves in an agricultural production system,with digital image processing techniques.

Brazil

[31]

Methods of vision-based row detection for lentil field

Iran

[32, 33]

Surveing methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum.

 

[34, 35, 36]

Developed automatic computer vision method for detecting Avena sterilis, a noxious weed growing in cereal crops, and differential spraying to control the weed

Spain

[37]

Evaluate the discrimination among three nutritional levels in wheat crop using digital images and a portable chlorophyll meter.

Brazil

[26]

Reviewed an automatic method for finding nitrogen and chlorophyll content in a plant based on leaf colour and image processing

India

[38]

Present a method based in computer vision for the construction of maps of application of variable rate herbicide dedicated to broad-leaved and narrow-leaf invasive plants of maize.

Brazil

 

[17]

Developed a machine vision system for a real time precision sprayer. From a monochrome CCD camera located in front of the tractor, the discrimination between crop and weeds is obtained with image processing based on spatial information using a Gabor filter.

 

France

[39-41]

Develop a machine vision system for a real-time precisión sprayer

France

[42]

Develop autonomous agricultural mobile robot for mechanical weed control in outdoor environments.

Sweden

[43]

Developed a moldy tobacco online detection system based on machine vision to realize automatic screening of mildew tobacco leaves .

China

[44]

Design and development of a camera-vision guided unmanned mover sprayer for the purpose of automatic weed control.

Malasya

[22]

Proposes an aerial images processing solution to be capable of identify exposed soil areas in large areas of plantations and can be embedded in a small computer and low power

 

 

Brazil

 

[21]

Developed an automatic counting system for urediospores of wheat stripe rust pathogen based on image processing using MATLAB GUIDE platform in combination with Local C Compiler (LCC)

China

 

[16]

Design of an expert system based on computer vision for real-time crop rows and weeds identification in maize fields. Furthermore, the proposed system controls the guidance of the tractor and the overlapping of the areas of treatment in order to apply a site-specific treatment

 

Spain

[45]

Developed a novelty system for obtaining the crop cover with easy, unattended and automated procedures from a digital photography ,the crop water needs are calculated from this photography combined with a mathematical algorithm.

Spain

[46]

Propose a new approach for automatic classification of weeds and crop digital images.

Brazil

[47]

Designed a sprayed to detect between the trees in orchrads using a machine vision system to stop the spraying on places where no tree exists.

Iran

 

[46]

Develop and evaluate the performance of an image processing system to identify weeds in sugarcane and estimate their level of infestation, since the existence of a computer tool to recognize plants species should give a great support to decision-making about the management of weed communities.

 

Brazil

[48]

Proposed a method to detect and count the number of white-flies using image processing on Simulink and Matlab software.

India

[49]

Discussed existing segmentation method along with classifiers for detection of plant leaves. A Survey On Detection Of Unhealthy Region Of Plant Leaves By Using Image Processing

India

[50]

Propose a system that performs vegetation detection, local as well as object-based feature extraction, random forest classification, and smoothing through a Markov random field to obtain an accurate estimate of the crops and weeds.

Germany

[51]

Propose a system that performs vegetation detection, plant-tailored feature extraction, and classification to obtain an estimate of the distribution of crops and weeds in the field. We implemented and evaluated our system using UAVs

Germany

[37]

Develop and evaluate an algorithm for identifying damaged corn plants by the fall armyworm (Spodoptera frugiperda) using digital color images

Brazil

[52]

Implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images.

Spain

[34]

Developed an automatic image processing technique to detect rice Crop height based on images taken by a digital camera attached to a field server.

Thailand

[53]

Presents a technique using computer vision to detect disease stress in wheat.

USA

[54]

Develop and evaluate a weeds and corn identification system,using color and monochomatics digital images

Brazil

[55]

Design and evaluate a novel dual camera sensor for use in an accurate single leaf level plant detection and classification system for weed control purposes.

UK

[56]

Developed a novel computer vision-based approach for automatically identifying crop diseases based on marker- controlled watershed segmentation, superpixel based feature analysis and classification

China

[44]

Used a digital camera to take pictures of the canopies of 3 rice (Oryza sativa L.) cultivars with 6 different nitrogen (N) application rates.

China

[57-59]

Implemented image processing using MATLAB to detect the weed areas in an image we took from the fields.

India

[60]

Proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle

Spain

 

[61]

Presents a low-cost computer vision system with the use of stereoscopic imaging and computational model dedicated to the recognition of small their variability, with particular application to those that present geometric primitives based on circular patterns.

 

Brazil

 

[62]

Proposed a fuzzy classification system using the attributes described to infer about the infestation risks of crop regions by weed plants. Simulation results of the proposed risk classification system are presented to illustrate its use in the site- specific herbicide application.

 

Brazil

 

[63]

Proposed method for locating and identifying weeds, using cotton as the example crop. The system used a digital video camera for capturing images along the crop seedline while simultaneously capturing data from a global positioning system (GPS) receiver

 

USA

[64-66]

Proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA).

Brazil

Table 1: Literarature Review about Artificial Vision in World Agriculture for Identification of diseases, pests, invasive plants