Deep Learning Based Crop Yield Prediction and Disease Identification DOI

M. Sambath,

Jaideep Kumar,

P. Santhosh

и другие.

Опубликована: Апрель 18, 2024

The research focuses on two important areas of agriculture: crop yield forecasts and the categorization plant diseases. We suggest using Graph Convolutional Networks (GCN), a deep learning method that makes use spatial correlations in photos to precisely identify illnesses, for Through analysis these linkages, GCN may improve agricultural production sustainability by facilitating early diagnosis prevention damage. simultaneously create time series model forecasting is based Transformers. This includes historical values pertinent contextual information, it displays data as sequences. Our goal achieve high accuracy predictions future training data. trained shows this strategy feasible, achieving 90.35% held-out test set. All things considered, publicly accessible, progressively larger picture datasets train models offers direct route widespread, smartphone-assisted disease detection.

Язык: Английский

Towards sustainable agriculture: Harnessing AI for global food security DOI Creative Commons
Dhananjay K. Pandey, Richa Mishra

Artificial Intelligence in Agriculture, Год журнала: 2024, Номер 12, С. 72 - 84

Опубликована: Апрель 30, 2024

The issue of food security continues to be a prominent global concern, affecting significant number individuals who experience the adverse effects hunger and malnutrition. finding solution this intricate necessitates implementation novel paradigm-shifting methodologies in agriculture sector. In recent times, domain artificial intelligence (AI) has emerged as potent tool capable instigating profound influence on sectors. AI technologies provide advantages by optimizing crop cultivation practices, enabling use predictive modelling precision techniques, aiding efficient monitoring disease identification. Additionally, potential optimize supply chain operations, storage management, transportation systems, quality assurance processes. It also tackles problem loss waste through post-harvest reduction, analytics, smart inventory management. This study highlights that how utilizing power AI, we could transform way produce, distribute, manage food, ultimately creating more secure sustainable future for all.

Язык: Английский

Процитировано

47
A systematic review of deep learning techniques for plant diseases DOI Creative Commons
İshak Paçal, İsmail Kunduracıoğlu, Mehmet Hakkı Alma

и другие.

Artificial Intelligence Review, Год журнала: 2024, Номер 57(11)

Опубликована: Сен. 30, 2024

Язык: Английский

Процитировано

25
Development of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach DOI Creative Commons
Midhun P Mathew, M. Sudheep Elayidom, V P Jagathy Raj

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 28, 2025

In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with transformer encoder blocks. This fusion enables accurate précised real-time classification of affecting grape, bell pepper, tomato plants. Incorporating blocks offers enhanced capability in capturing intricate spatial dependencies within images, promising agricultural sustainability food security. By providing farmers farming stakeholders reliable tool rapid disease detection, our facilitates timely intervention management practices, ultimately leading to improved yields mitigated economic losses. Through extensive comparative analyses on various datasets filed tests, proposed depth wise separable convolutional-TransNet (DSC-TransNet) has demonstrated higher performance terms accuracy (99.97%), precision (99.94%), recall (99.94), sensitivity F1-score AUC (0.98) Grpae leaves across different including pepper tomato. Furthermore, DSC layers enhances computational efficiency while maintaining expressive power, making it well-suited applications. The developed DSC-TransNet deployed NVIDIA Jetson Nano single board computer. contributes advancing field automated plant classification, addressing critical challenges modern agriculture promoting more efficient practices.

Язык: Английский

Процитировано

1
Leveraging Convolutional Neural Networks for Disease Detection in Vegetables: A Comprehensive Review DOI Creative Commons
Muhammad Mahmood ur Rehman,

Jizhan Liu,

Aneela Nijabat

и другие.

Agronomy, Год журнала: 2024, Номер 14(10), С. 2231 - 2231

Опубликована: Сен. 27, 2024

Timely and accurate detection of diseases in vegetables is crucial for effective management mitigation strategies before they take a harmful turn. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools automated disease crops due to their ability learn intricate patterns from large-scale image datasets make predictions samples that are given. The use CNN algorithms important vegetable like potatoes, tomatoes, peppers, cucumbers, bitter gourd, carrot, cabbage, cauliflower critically examined this review paper. This examines the most state-of-the-art techniques, datasets, difficulties related these crops’ CNN-based systems. Firstly, we present summary architecture its applicability classify tasks based on images. Subsequently, explore applications identification crops, emphasizing relevant research, performance measures. Also, benefits drawbacks methods, covering problems with computational complexity, model generalization, dataset size, discussed. concludes by highlighting revolutionary potential transforming crop diagnosis strategies. Finally, study provides insights into current limitations regarding usage computer field detection.

Язык: Английский

Процитировано

6
Innovative Deep Learning Framework for Accurate Plant Disease Detection and Crop Productivity Enhancement DOI

M. Mohan,

S. Anandamurugan

Cognitive Computation, Год журнала: 2025, Номер 17(1)

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

0
Synthesis and Activity of Novel Pyrazole/Pyrrole Carboxamides Containing a Dinitrogen Six-Membered Heterocyclic as Succinate Dehydrogenase and Ergosterol Biosynthesis Inhibitors against Colletotrichum camelliae DOI

Kuai Chen,

Dandan Song,

D. Shi

и другие.

Journal of Agricultural and Food Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Апрель 23, 2025

Pyrazole carboxamide derivatives were initially extensively studied as succinate dehydrogenase inhibitors (SDHIs). In the present study, a series of pyrazole/pyrrole carboxamides containing dinitrogen six-membered heterocyclic designed based on our reported active skeletons with dual mode action. Bioactivity results showed that target compound Q18 demonstrated superior antifungal efficacy against Colletotrichum camelliae (C. camelliae) an EC50 value 6.0 mg/L. The in vivo protective activity was 74.7% at 100 Scanning electron microscopy and transmission could disrupt surface morphology mycelia cause lipid peroxidation cell membrane, which further verified by determination relative conductivity malondialdehyde contents. Combined ergosterol content, docking between SDH CYP51, IC50 for (9.7 mg/L), it is concluded potential SDHI biosynthesis inhibitor. Thus, study provides fresh insight into amides.

Язык: Английский

Процитировано

0
Optimizing Edge AI for Tomato Leaf Disease Identification DOI Open Access

Anitha Gatla,

S. R. V. Prasad Reddy,

D. Mândru

и другие.

Engineering Technology & Applied Science Research, Год журнала: 2024, Номер 14(4), С. 16061 - 16068

Опубликована: Авг. 2, 2024

This study addresses the critical challenge of real-time identification tomato leaf diseases using edge computing. Traditional plant disease detection methods rely on centralized cloud-based solutions that suffer from latency issues and require substantial bandwidth, making them less viable for applications in remote or bandwidth-constrained environments. In response to these limitations, this proposes an on-the-edge processing framework employing Convolutional Neural Networks (CNNs) identify diseases. approach brings computation closer data source, reducing conserving bandwidth. evaluates various pre-trained models, including MobileNetV2, InceptionV3, ResNet50, VGG19 against a custom CNN, training validating comprehensive dataset images. MobileNetV2 demonstrated exceptional performance, achieving accuracy 98.99%. The results highlight potential AI revolutionize agricultural settings, offering scalable, efficient, responsive solution can be integrated into broader smart farming systems. not only improves but also provide actionable insights timely alerts farmers, ultimately contributing increased crop yields food security.

Язык: Английский

Процитировано

2
Modified ensemble machine learning-based plant leaf disease detection model with optimized K-Means clustering DOI
Vijay Viswanathan,

Krishnamoorthi Murugasamy

Network Computation in Neural Systems, Год журнала: 2024, Номер unknown, С. 1 - 45

Опубликована: Дек. 9, 2024

In the farming sector, automatic detection of plant leaf disease is considered a vital landmark. Farmers move long distances to consult pathologists observe disease, which expensive and time-consuming. Moreover, in premature period difficult process existing model. Thus, all these challenges motivate us develop an inventive developed model, data gathered initially given as input pre-processing step using Contrast Limited Adaptive Histogram Equalization (CLAHE). Next, leaves are segmented from pre-processed images, then abnormality segmentation done by K-means clustering system. Here, parameters optimized Opposition-based Bird Swarm Algorithm (O-BSA). Further, features were extracted abnormality-segmented images feature extraction. The classification step, where carried out Optimized Ensemble Machine Learning (OEML), where, parameter optimization O-BSA. Finally, approach evaluated with various performance metrics, accuracy up 92.26. These findings show that model promising over conventional methods its effectiveness detecting disease.

Язык: Английский

Процитировано

1
Deep Learning Based Crop Yield Prediction and Disease Identification DOI

M. Sambath,

Jaideep Kumar,

P. Santhosh

и другие.

Опубликована: Апрель 18, 2024

The research focuses on two important areas of agriculture: crop yield forecasts and the categorization plant diseases. We suggest using Graph Convolutional Networks (GCN), a deep learning method that makes use spatial correlations in photos to precisely identify illnesses, for Through analysis these linkages, GCN may improve agricultural production sustainability by facilitating early diagnosis prevention damage. simultaneously create time series model forecasting is based Transformers. This includes historical values pertinent contextual information, it displays data as sequences. Our goal achieve high accuracy predictions future training data. trained shows this strategy feasible, achieving 90.35% held-out test set. All things considered, publicly accessible, progressively larger picture datasets train models offers direct route widespread, smartphone-assisted disease detection.

Язык: Английский

Процитировано

0