A deep learning algorithm for detection of potassium deficiency in a red grapevine and spraying actuation using a raspberry pi3

Ukaegbu, Uchechi; Tartibu, Lagouge K.; Laseinde, Opeyeolu Timothy; Okwu, Modestus O.; Olayode, Isaac Oyeyemi

doi:10.1109/icabcd49160.2020.9183810

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…The used dataset contains six varieties of 50 red grapevine leaf images. The performance of the model was compared with the Support Vector Machine (SVM) algorithm for which the leaf features were extracted with the Histogram of Oriented Gradients (HOG) descriptor and the obtained accuracy was 66.67% [10]. The authors have used limited leaf images.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Nutrient Deficiency Prediction System for Groundnut Leaf

Malaisamy¹,

Jebakumar²

2023

Intelligent Automation &Amp; Soft Computing

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Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative yield. Distributing fertiliser in optimum amounts will protect the environment's condition and human health risks. Early identification also prevents the disease's occurrence in groundnut crops. A convolutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitrogen nutrient deficiency through image features. Since chlorophyll and nitrogen are proportionate to one another, the Smart Nutrient Deficiency Prediction System (SNDP) is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be known. The model's first part is to perform preprocessing using Groundnut Leaf Image Preprocessing (GLIP). Then, in the second part, feature extraction using a convolution process with Non-negative ReLU (CNNR) is done, and then, in the third part, the extracted features are flattened and given to the dense layer (DL) layer. Next, the Maximum Margin classifier (MMC) is deployed and takes the input from DL for the classification process to find CCR. The dataset used in this work has no visible symptoms of a deficiency with three categories: low level (LL), beginning stage of low level (BSLL), and appropriate level (AL). This model could help to predict nitrogen deficiency before perceivable symptoms. The performance of the implemented model is analysed and compared with ImageNet pre-trained models. The result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99% and 95%, respectively, compared to existing pre-trained models.

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Section: Literature Surveymentioning

confidence: 99%

Smart Nutrient Deficiency Prediction System for Groundnut Leaf

Malaisamy¹,

Jebakumar²

2023

Intelligent Automation &Amp; Soft Computing

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“…Over the last two decades, we have seen a significant increase in the discussion of the Fourth Industrial Revolution (4IR) among academics and policymakers in both developing and industrialized countries [1]. The 4IR critique is marked by the merging of the real and virtual worlds, and the collapse of almost all industries [1,2]. For others, the assembling of cyber-physical systems, cloud technology, the internet of things, internet of services; integrating them, while interacting with humans in real-time to maximize the generation of value, is known as Fourth Industrial Revolution [3].…”

Section: Introductionmentioning

confidence: 99%

“…For others, the assembling of cyber-physical systems, cloud technology, the internet of things, internet of services; integrating them, while interacting with humans in real-time to maximize the generation of value, is known as Fourth Industrial Revolution [3]. Some thinkers assert that some old jobs will vanish because of the alleged revolutionary power of 4IR, opening the door for a new array of jobs and markets that will necessitate the creation of new areas of expertise [1][2][3]. The word "fourth" typically implies that there have been three revolutions before industrial revolution 4.0 [3].…”

Section: Introductionmentioning

confidence: 99%

“…In the 1970s, it was believed that automating repetitive tasks would liberate people, resulting in more free time and less working time [1]. Despite advancements in technology, this promise remained mostly unmet [1,2]. Now, the Fourth Industrial Revolution, which builds on digitalization and information and communication technologies (ICT), is thought to revolutionize everything [6].…”

Section: Introductionmentioning

confidence: 99%

“…Even though 4IR has been the topic of discussion on many international forums, there haven't been many systematic attempts to analyze the state of the art of this new industrial revolution wave [6]. This situation may be more apparent in Africa, where the third industrial revolution has mostly not even fully begun [1][2][3]. Therefore, African academics have expressed scepticism and caution over the alleged advantages of Information and Communications Technology (ICT) in African environments.…”

Section: Introductionmentioning

confidence: 99%

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Maximizing a Farm Yield Through Precision Agriculture utilizing Fourth Industrial Revolution (4IR) Tools and Space Technology

Ngongoma¹,

Musasa²,

Moloi³

2023

Preprint

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The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

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Leveraging Computer Vision for Precision Viticulture

Vrochidou

Papakostas

2023

Computer Vision and Machine Learning in Agriculture, Volume 3

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A deep learning algorithm for detection of potassium deficiency in a red grapevine and spraying actuation using a raspberry pi3

Cited by 13 publications

References 21 publications

Smart Nutrient Deficiency Prediction System for Groundnut Leaf

Smart Nutrient Deficiency Prediction System for Groundnut Leaf

Maximizing a Farm Yield Through Precision Agriculture utilizing Fourth Industrial Revolution (4IR) Tools and Space Technology

Leveraging Computer Vision for Precision Viticulture

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