Proceedings ArticleDOI
Tomato Septoria Leaf Spot Necrotic and Chlorotic Regions Computational Assessment Using Artificial Bee Colony-Optimized Leaf Disease Index
Ronnie Concepcion,Sandy Lauguico,Elmer P. Dadios,Argel A. Bandala,Edwin Sybingco,Jonnel Alejandrino +5 more
- pp 1243-1248
TLDR
In this paper, the integration of computer vision and computational intelligence for tomato Septoria leaf spot necrotic and chlorotic region computational assessment is proposed, where a new tomato leaf disease index (tomLDI) optimized using artificial bee colony (ABC) was developed by normalizing visible red reflectance, and introducing red-green and red-blue reflectance ratios to enhance tomato leaf spots pixels and reducing sensitivity to healthy green pixels.Abstract:
Visual inspection of plant health status and disease severity may yield subjective assessments due to error-prone sphere of colors and textures as affected by angular photosynthetic light source and the complexity of chlorosis. Quantification of damages on leaves due to destructive diseases is paramount for plant and pathogen interactions. To address this challenge, the proposed solution is the integration of computer vision and computational intelligence for tomato Septoria leaf spot necrotic and chlorotic region computational assessment. Dataset contains healthy and diseased tomato leaves that were captured individually. Non-vegetation pixels removal was done using CIELab color space. RGB color components and five Haralick texture features were extracted from the segmented leaf. Hybrid neighborhood component analysis and ReliefF algorithm were employed to select the important predictors resulting to RGB-entropy vector. A new tomato leaf disease index (tomLDI) optimized using artificial bee colony (ABC) was developed by normalizing visible red reflectance, and introducing red-green and red-blue reflectance ratios to enhance Septoria leaf spots pixels and reducing sensitivity to healthy green pixels. KNN bested classification tree, linear discriminant analysis and Naive Bayes in detecting Septoria leaf disease with accuracy of 97.46%. Deep transfer image regression was tested using raw infected leaf images and the tomLDI transformed colored channels through MobileNetV2, ResNet101 and InceptionV3. Using tomLDI channel, MobileNetV2 and ResNet101 bested other networks in estimating leaf diseased region percentage and number of Septoria spots with R2 values of 0.9930 and 0.9484 respectively. tomLDI channel proved to be more accurate than using raw images for regression.read more
Citations
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Proceedings ArticleDOI
Trend Forecasting of Computer Vision Application in Aquaponic Cropping Systems Industry
TL;DR: In this article, the authors discussed the international and local agricultural advancements and reviewed the trends of computational intelligence (CI) applications in aquaponics systems, which can help boost the industry performance especially now that the whole world is now engaging in the fourth industrial reform (FIRe).
Proceedings ArticleDOI
Trends in Nanotechnology in the Philippines and Laos Agricultural Industry
Lue Xiong,Ronnie Concepcion,Gil Nonato C. Santos,Jeremias A. Gonzaga,Laurence A. Gan Lim,Elmer P. Dadios +5 more
TL;DR: In this article, a study of nanotechnology in the Philippines and Laos agricultural industry is explored and analyzed, on the agricultural system to be quality and safety food, increase technical capacity inputs of agricultural less soil by using nutrients management.
Proceedings ArticleDOI
A Look at the Near Future: Industry 5.0 Boosts the Potential of Sustainable Space Agriculture
Ronnie Concepcion,Jonnel Alejandrino,Adrian Genevie G. Janairo,Jonah Jahara Baun,Kate G. Francisco,R-Jay S. Relano,Mike Louie C. Enriquez,Ryan Rhay P. Vicerra,Argel A. Bandala,Luigi Gennaro Izzo +9 more
TL;DR: In this article , the authors discuss the comparative differences of Industrial Revolutions 4.0 and 5.0 in relation to its impacts to farm labor, cultivation and post-harvest system, green products and services, and agricultural supply chain.
Proceedings ArticleDOI
On-demand Healthy and Chlorotic Lactuca sativa Leaf Classification Using Support Vector Machine in a Rotating Hydroponic System
TL;DR: In this paper , the authors proposed a method for evaluating healthy and chlorotic lettuce leaves produced in rotating hydroponics setups using computational intelligence and computer vision, and a total of 533 image sets (273 healthy and 260 chlorotic leaves) were utilized.
Proceedings ArticleDOI
On-demand Healthy and Chlorotic Lactuca sativa Leaf Classification Using Support Vector Machine in a Rotating Hydroponic System
Heinrick L. Aquino,Edwin Sybingco,Christan Hail Mendigoria,Ronnie Concepcion,Argel A. Bandala,Oliver John Y. Alajas,Elmer P. Dadios,Ryan Rhay P. Vicerra +7 more
TL;DR: In this article , the authors proposed a method for evaluating healthy and chlorotic lettuce leaves produced in rotating hydroponics setups using computational intelligence and computer vision, and a total of 533 image sets (273 healthy and 260 chlorotic leaves) were utilized.
References
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Proceedings ArticleDOI
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Journal ArticleDOI
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