A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks

Sambasivam, G.; Opiyo, Geoffrey Duncan

doi:10.1016/j.eij.2020.02.007

Cited by 218 publications

(73 citation statements)

References 25 publications

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“…Using an optimally designed AI system during crop growth period not only reduces the risk of crop disease and minimizes the economic impact, but it also results in minimizing the adverse impact of unsystematic farming on the environment. Sambasivan and Opiyo [71] used a CNN based DL model to detect disease in cassava crops for imbalanced datasets. The authors took a database of 10,000 labeled images that were preprocessed to improve the image contrast using contrast limited adaptive histogram equalization algorithm.…”

Section: Disease and Weed Detectionmentioning

confidence: 99%

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

et al. 2021

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Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

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Section: Disease and Weed Detectionmentioning

confidence: 99%

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

et al. 2021

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“…Image analysis has similarly been used ( Garcia-Oliveira et al, 2020 ; Nakatumba-Nabende et al, 2020 ). Analysis of field images combined with various algorithms, including K mean clustering algorithms ( Anderson et al, 2015 ), artificial neutral network ( Abdullakasim et al, 2011 ), and more recently, machine learning techniques and convolutional neutral network ( Owomugisha and Mwebaze, 2016 ; Sambasivam and Opiyo, 2020 ) have provided a more accurate and objective assessment of disease severity and incidence. The smart phone-based diagnostic system (NURU-AI) is being developed to support remote diagnosis by smallholder farmers in Africa for real-time prediction of the state of cassava health ( Owomugisha and Mwebaze, 2016 ; Ramcharan et al, 2017 , 2019 ).…”

Section: Phenotyping Of Key Traitsmentioning

confidence: 99%

Technological Innovations for Improving Cassava Production in Sub-Saharan Africa

et al. 2021

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Cassava is crucial for food security of millions of people in sub-Saharan Africa. The crop has great potential to contribute to African development and is increasing its income-earning potential for small-scale farmers and related value chains on the continent. Therefore, it is critical to increase cassava production, as well as its quality attributes. Technological innovations offer great potential to drive this envisioned change. This paper highlights genomic tools and resources available in cassava. The paper also provides a glimpse of how these resources have been used to screen and understand the pattern of cassava genetic diversity on the continent. Here, we reviewed the approaches currently used for phenotyping cassava traits, highlighting the methodologies used to link genotypic and phenotypic information, dissect the genetics architecture of key cassava traits, and identify quantitative trait loci/markers significantly associated with those traits. Additionally, we examined how knowledge acquired is utilized to contribute to crop improvement. We explored major approaches applied in the field of molecular breeding for cassava, their promises, and limitations. We also examined the role of national agricultural research systems as key partners for sustainable cassava production.

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“…Image processing is a key point for the visual cue of a robot used in a place where it is necessary to recognize an object, such as an agricultural robot [8,9,10,11,12,13,14,15,16,17]. Image processing is helpful in any agricultural situation, such as seeding [8][9], maintenance [18] [19], yield forecast [20][21], harvesting [12], and packaging [17,22,23,24,25,26,27]. The use of robots in agriculture could ease the farmer's work burden and increase the harvest yield [8].…”

Section: Introductionmentioning

confidence: 99%

Motion Control Analysis of Two Collaborative Arm Robots in Fruit Packaging System

Dewi

Anggraini

Risma

et al. 2021

Sinergi

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As robots' use increases in every sector of human life, the demand for cheap and efficient robots has also enlarged. The use of two or more simple robot is preferable to the use of one sophisticated robot. The agriculture industry can benefit from installing a robot, from seeding to the packaging of the product. A precise analysis is required for the installation of two collaborative robots. This paper discusses the motion control analysis of two collaborative arms robots in the fruit packaging system. The study begins with the relative motion analysis between two robots, starting with kinematics modeling, image processing for object detection, and the Fuzzy Logic Controller's design to show the relationship between the robot inputs and outputs. The analysis is carried out using SCILAB, open-source software for numerical computing engineering. This paper is intended as the initial analysis of the feasibility of the real experimental system.

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A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks

Cited by 218 publications

References 25 publications

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Technological Innovations for Improving Cassava Production in Sub-Saharan Africa

Motion Control Analysis of Two Collaborative Arm Robots in Fruit Packaging System

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