Detection of Diseases in Tomato Leaves by Color Analysis

Luna-Benoso, B.; Martinez-Perales, J. C.; Cortes-Galicia, J.; Flores-Carapia, R.; Silva-García, Víctor Manuel

doi:10.3390/electronics10091055

Cited by 13 publications

(5 citation statements)

References 45 publications

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“…LITERATURE REVIEW [1] In the investigation led by Benjamín Luna Benoso, José Cruz Martínez-Perales, Jorge Cortés-Galicia, Rolando Flores-Carapia, and Víctor Manuel Silva-García on the detection of diseases in tomato leaves through color analysis and GLCM methodology, the study identifies a challenge marked by low accuracy in effectively distinguishing between healthy and diseased leaves. [2] In the work conducted by Mohammed Brahimi, Kamek Boukhalfa, and Abdelouahab Moussaoui on the classification and visualization of symptoms of tomato diseases, challenges are evident with low accuracy and limited feature extraction.…”

Section: IImentioning

confidence: 99%

Tomato Leaves Disease Detection using Hybrid Model CNN-LSTM

Chandran

2024

IJRASET

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This paper delves into the exploration and comparison of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model for the task of tomato leaf disease detection. Through rigorous experimentation and evaluation, it was determined that the CNN-LSTM hybrid model demonstrates superior performance both CNN and LSTM individually in terms of accuracy and robustness. Leveraging a comprehensive dataset comprising images of healthy and diseased tomato leaves, including various disease types, such as Bacterial Spot, Early Blight, Septorial Leaf Spot, Leaf Mold, and Yellow Leaf Curl Virus, the study demonistrate the efficacy of the suggested hybrid approach. The hybrid model capitalizes on the strengths of both CNN and LSTM, integrating spatial and temporal information for enhanced disease identification accuracy. The results highlight the effect of hybrid deep learning structures in agricultural disease management, offering a promising solution for early detection and mitigation of plant diseases. This paper dds to the expanding collection of literature on deep learning applications in agriculture and offers valuable insights for researchers and practitioners seeking to address challenges in crop health monitoring and management.

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Section: IImentioning

confidence: 99%

Tomato Leaves Disease Detection using Hybrid Model CNN-LSTM

Chandran

2024

IJRASET

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“…Their proposed model has an accuracy of 99.53%. Benjamín Luna-Benoso et al [17], performed a negative operator and median filter on a grayscale computed image, and then thresholding is applied using the Otsu method. After that, nine features were computed along with four color moments.…”

Section: Jagadeesh D Pujari Et Al Used Glcm and Graymentioning

confidence: 99%

Real time vision-based implementation of plant disease identification system on FPGA

Ahmed

Zaidi

Aziz

et al. 2023

Mehran Univ. res. j. eng. technol.

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Plant diseases have turned into a dilemma as it can cause significant reduction in both quality and quantity of agricultural products. To overcome that loss, we implemented a computer vision based real time system that can identify the type of plant diseases. Computer vision-based applications are computationally intensive and time consuming, so FPGA-based implementation is proposed to have a real time identification of plant diseases. In this paper an image processing algorithm is proposed for identifying two types of disease in Potato leaves. The proposed algorithm works well on images taken under different luminance conditions. The hardware/software-based implementation of the proposed algorithm is done on Xilinx ZYNQ SoC FPGA. Results show that our proposed algorithm achieves an accuracy of up to 90%, whereas the hardware implementation takes 0.095 seconds achieving a performance gain of 76.8 times as compared to the software implementation.

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“…Refs. [3][4][5] use Internet of Things technology to achieve intelligent agriculture to achieve real-time monitoring. CFMBs may also be applied in the future in the fields of medicine, biology, environmental monitoring, food safety, and timely elimination of possible hidden dangers.…”

Section: Introductionmentioning

confidence: 99%

Architectural Synthesis of Continuous-Flow Microfluidic Biochips with Connection Pair Optimization

Hu,

Chen,

Chen

et al. 2024

Electronics

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Continuous-flow microfluidic biochips are a type of biochip technology based on microfluidic channels that enable various biological experiments and analyses to be performed on a tiny chip. They have the advantages of a high throughput, high sensitivity, high precision, low cost, and quick response. In the architectural synthesis of continuous-flow microfluidic biochips (CFMBs), prior work has not considered reducing component interconnection requirements, which led to an increase in the number of connection pairs. In this paper, we propose an architectural synthesis flow for continuous-flow microfluidic biochips with connection pair optimization, which includes high-level synthesis, placement, and routing. In the high-level synthesis stage, our method reduces the need for component interconnections, which reduces the number of connection pairs. Our method performs fine-grained binding, ultimately obtaining high-quality binding and scheduling results for flow paths. Based on the high-quality binding results, we propose a port placement strategy based on port correlation and subsequently use a quadratic placer to place the components. During the routing stage, we employ a conflict-aware routing algorithm to generate flow channels to reduce conflicts between liquid transportation tasks. Experimental results on multiple benchmarks demonstrate the effectiveness of our method. Compared with the existing work, the proposed algorithm obtains average reductions of 35.34% in connection pairs, 24.30% in flow channel intersections, 21.71% in total flow channel length, and 18.39% in the execution time of bioassays.

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Detection of Diseases in Tomato Leaves by Color Analysis

Cited by 13 publications

References 45 publications

Tomato Leaves Disease Detection using Hybrid Model CNN-LSTM

Tomato Leaves Disease Detection using Hybrid Model CNN-LSTM

Real time vision-based implementation of plant disease identification system on FPGA

Architectural Synthesis of Continuous-Flow Microfluidic Biochips with Connection Pair Optimization

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