Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on AWS DeepLens

Khan, Asim; Nawaz, Umair; Ulhaq, Anwaar; Robinson, Ross

doi:10.20944/preprints202009.0142.v3

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditionally, either farmers are manually classifying the diseases or pathologist are identifying the disease through lab experiments. However, the performance of traditional systems is purely depending on their experience, and it also a time-consuming task [11,12]. Further, the early detection and prevention of plant diseases can improve the hydroponics performance.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Smart Hydroponics Farming Using IoT‐Based AI Controller with Mobile Application System

Raju¹,

Dappuri²,

Varma

et al. 2022

Journal of Nanomaterials

View full text Add to dashboard Cite

Hydroponics is the soil less agriculture farming, which consumes less water and other resources as compared to the traditional soil-based agriculture systems. However, monitoring of hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters, nutrition suggestion, and plant diagnosis system. But the recent technological developments are quite useful to solve these problems by adopting the artificial intelligence-based controlling algorithms in agriculture sector. Therefore, this article focuses on implementation of mobile application integrated artificial intelligence based smart hydroponics expert system, hereafter referred as AI-SHES with Internet of Things (IoT) environment. The proposed AI-SHES with IoT consists of three phases, where the first phase implements hardware environment equipped with real-time sensors such as NPK soil, sunlight, turbidity, pH, temperature, water level, and camera module which are controlled by Raspberry Pi processor. The second phase implements deep learning convolutional neural network (DLCNN) model for best nutrient level prediction and plant disease detection and classification. In third phase, farmers can monitor the sensor data and plant leaf disease status using an Android-based mobile application, which is connected over IoT environment. In this manner, the farmer can continuously track the status of his field using the mobile app. In addition, the proposed AI-SHES also develops the automated mode, which makes the complete environment in automatic control manner and takes the necessary actions in hydroponics field to increase the productivity. The obtained simulation results on disease detection and classification using proposed AI-SHES with IoT disclose superior performance in terms of accuracy, F-measure with 99.29%, and 99.23%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Smart Hydroponics Farming Using IoT‐Based AI Controller with Mobile Application System

Raju¹,

Dappuri²,

Varma

et al. 2022

Journal of Nanomaterials

View full text Add to dashboard Cite

show abstract

“…While this paradigm addresses the large round-trip times of Cloud Computing, the QoS is now limited due to the capacity of the edge devices. Not only academia, but lately industry has placed focus on Edge Computing, by providing software (e.g., Google Lite TensorFlow (Demosthenous and Vassiliades, 2021)©) and hardware (e.g., NVIDIA Jetson AGX Xavier© (Hossain and Lee, 2019) and AWS DeepLens© (Khan et al, 2020)) solutions are suitable for edge processing.…”

Section: Introductionmentioning

confidence: 99%

Enabling artificial intelligence analytics on the edge

Τσακανίκας¹

View full text Add to dashboard Cite

Αυτή η διατριβή εισάγει ένα νέο κατανεμημένο μοντέλο για χειρισμό σε πραγματικό χρόνο, αναλυτικών βίντεο στα άκρα του δικτύου. Η καινοτομία του μοντέλου βασίζεται στην αποσύνδεση και τη διανομή των υπηρεσιών σε πολλές αποσυντεθειμένες συναρτήσεις, δημιουργώντας αλυσίδες εικονικών λειτουργιών (μοντέλο VFC). Το μοντέλο λαμβάνει υπόψη τόσο τους υπολογιστικούς περιουρισμούς όσο και τους περιορισμούς επικοινωνίας. Θεωρητικά αποτελέσματα, αποτελέσματα προσομοιώσεων και πειραματικά αποτελέσματα έχουν δείξει ότι το μοντέλο VFC μπορεί να επιτρέψει την υποστήριξη υπηρεσιών βαρέως φορτίου σε περιβάλλον στα άκρα του δικτύου βελτιώνοντας παράλληλα το αποτύπωμα της υπηρεσίας σε σύγκριση με πλαίσια τελευταίας τεχνολογίας. Αναλυτικά, τα αποτελέσματα στο μοντέλο VFC έδειξαν ότι μπορεί να μειώσει το συνολικό κόστος των άκρων, σε σύγκριση με μοντέλα διανομής μονολιθικού και απλού πλαισίου. Για πειραματισμούς σε ένα πραγματικό σενάριο, έχει αναπτυχθεί ένα δοκιμαστικό περιβάλλον δικτύου στα άκρα, όπου έχουν αναπτυχθεί τα προαναφερθέντα μοντέλα, καθώς και ένα γενικό πλαίσιο διανομής (Apache Spark ©). Εξετάστηκε επίσης μια υπηρεσία υπολογιστικού νέφους. Τα πειράματα έχουν δείξει ότι το VFC μπορεί να ξεπεράσει όλες τις εναλλακτικές προσεγγίσεις, μειώνοντας το λειτουργικό κόστος και βελτιώνοντας την ποιότητα της υπηρεσίας. Τέλος, εισάγεται ένα μοντέλο μετεγκατάστασης, ένα μοντέλο προσωρινής αποθήκευσης και μια υπηρεσία παρακολούθησης της ποιότητας υπηρεσίας που βασίζεται σε μοντέλα τεχνητής νοημοσύνης (LSTM).

show abstract

The Evaluation of the Grade of Leaf Disease in Apple Trees Based on PCA-Logistic Regression Analysis

Xing

Dian

Yin

2023

Forests

View full text Add to dashboard Cite

Extensive research suggested that the core of how to use pesticides scientifically is the careful and accurate determination of the severity of crop diseases. The existing grading standards of plant leaf diseases have been excessively singular. Thus, the diseases roughly fall into general and severe grades. To address the above problems, this study considered the effect of the distribution of disease spots, and two evaluation indicators (termed the imbalance degree and main vein distance) were newly added to optimize the grading criteria of apple leaf diseases. Combined with other factors, the grade evaluation indicator was determined through PCA principal component analysis. A gradual multivariate logistic regression algorithm was proposed to evaluate apple leaf disease grade and an optimized apple leaf disease grade evaluation model was built through PCA-logistic regression analysis. In addition, three common apple leaf diseases with a total of 4500 pictures (i.e., black rot, scab, and rust) were selected from several open-source datasets as the subjects of this paper. The object detection algorithm was then used to verify the effectiveness of the new model. As indicated by the results, it can be seen from the loss curve that the loss rate reaches a stable range of around 70 at the epoch. Compared with Faster R-CNN, the average accuracy of Mask R-CNN for the type and grade recognition of apple leaf disease was optimized by 4.91%, and the average recall rate was increased by 5.19%. The average accuracy of the optimized apple leaf disease grade evaluation model was 90.12%, marking an overall increase of 20.48%. Thus, the effectiveness of the new model was confirmed.

show abstract

Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on AWS DeepLens

Cited by 3 publications

References 10 publications

Design and Implementation of Smart Hydroponics Farming Using IoT‐Based AI Controller with Mobile Application System

Design and Implementation of Smart Hydroponics Farming Using IoT‐Based AI Controller with Mobile Application System

Enabling artificial intelligence analytics on the edge

The Evaluation of the Grade of Leaf Disease in Apple Trees Based on PCA-Logistic Regression Analysis

Contact Info

Product

Resources

About