Source Raw Data More Information ::

PlantVill dataset,PlantVillage dataset,https://www.kaggle.com/datasets/mohitsingh1804/plantvillage PlantVillage-AD (Augmented Dataset)

Shuvo Kumar Basak. (2025). Plant Village Augmented Dataset New and Update Tec [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/11077233

The Plant Village Augmented Dataset is an enhanced version of the original PlantVillage dataset, designed to provide a more diverse and comprehensive collection of images for plant disease detection. This augmented dataset includes a variety of image processing techniques, such as edge enhancement, noise addition, and transformations like rotation, flipping, and scaling. It also incorporates adjustments to brightness, contrast, and saturation, helping to simulate real-world conditions and improve model robustness. The dataset contains images of healthy and diseased plant leaves across multiple species, making it ideal for training and evaluating machine learning models for plant health monitoring and disease classification.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F9ad4aea3445f6e43b5a6f5e7981f8e06%2F_results_2_0.png?generation=1742438764723367&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F71166fe9ca1c6a2c33d9d4d1b5cbcac1%2F_results_7_0.png?generation=1742438779439472&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F5fbd37c9a81a927e72eba6b1bed1a315%2F_results_5_0.png?generation=1742438798463325&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F0f151b595e4a5e1d4d89f9a87c03ac63%2F_results_4_0.png?generation=1742438810704589&alt=media" alt="">

Source Raw Data More Information ::

https://www.kaggle.com/datasets/mohitsingh1804/plantvillage PlantVill dataset,PlantVillage dataset,https://www.kaggle.com/datasets/mohitsingh1804/plantvillage PlantVillage-AD (Augmented Dataset)

Shuvo Kumar Basak. (2025). Plant Village Augmented Dataset New and Update Tec [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/11077233

**More Dataset:: ** https://www.kaggle.com/shuvokumarbasak4004/datasets

…………………………………..Note for Researchers Using the dataset………………………………………………………………………

If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.

Tomato Leaf Disease Classification

About 22,000 images of tomato leaves with a size of 256x256., covering 9 diseases and a healthy leaf category. The images have been captured in both laboratory settings and real-world scenarios and are already data-augmented. The goal is to develop a lightweight learning model capable of predicting tomato leaf diseases that can be implemented in a mobile application for offline diagnostics.

Classes: - bacterial_spot
- early_blight
- healthy - late_blight - leaf_mold - mosaic_virus - septoria_leaf_spot - target_spot - twospotted_spider_mite - yellow_leaf_curl_virus

A function has been used to discard all blurred images from the original data set to ensure better accuracy in the real world, so the number of total images with respect to version 1 is somewhat smaller:

def is_blurry(image_path, threshold=100):
  # Load image
  image = cv2.imread(image_path)
  
  # Convert image to grayscale
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  
  # Calculate the value of the image blur using the Laplacian variance
  variance = cv2.Laplacian(gray, cv2.CV_64F).var()
  
  # If the variance is less than the threshold, the image is considered blurred.
  return variance < threshold

To increase data in the original images, the following configuration has basically been used:

  datagen = image.ImageDataGenerator(
    width_shift_range=0.12,
    height_shift_range=0.12,
    shear_range=0.12,
    zoom_range=0.12,
    horizontal_flip=True,
    vertical_flip=True,
    rotation_range=60,
    fill_mode='constant',
    cval=0,
    brightness_range=[0.80, 1.42])

All original images come from only 2 sources:

The training dataset for the disease prediction model comprises more than 500 images capturing both healthy and diseased tomato and wheat leaves. The validation and test datasets included over 120 and 50 images respectively. The dataset was meticulously curated by manually collecting data from diverse sources, including the Plant Village dataset, Plant Doc dataset, and Wheat Leaf dataset.

From July to August 2022, we collected the data of string tomato in the glass greenhouse in Shanxi Nonggu Tomato Town, Getou Village, Fancun Town, Taigu District, Jinzhong City, Shanxi Province. We took pictures of string tomato in different angles and directions with different models of mobile phones in different light positions on sunny days, cloudy days, and different time periods. After sorting and screening, we selected 3665 images with a size of 5.31GB. LabelImg tool is used to label the selected images with four types of labels: mature, raw, stem, and cover, which are stored as TXT documents supporting yolo format, with a size of 0.8MB.

Classification accuracy results for each class in the tomato plant disease dataset.

Performance analysis of various model on the tomato plant dataset.

Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges from various sources, including environmental (abiotic stress) and biological (biotic stress or disease) threats, which adversely impact the crop’s growth, reproduction, and overall yield potential. The objective of this work is to build deep learning based lightweight convolutional neural network (CNN) architecture for the real-time classification of biotic stress in tomato plant leaves. This model proposes to address the drawbacks of conventional CNNs, which are resource-intensive and time-consuming, by using optimization methods that reduce processing complexity and enhance classification accuracy. Traditional plant disease classification methods predominantly utilize CNN based deep learning techniques, originally developed for fundamental image classification tasks. It relies on computationally intensive CNNs, hindering real-time application due to long training times. To address this, a lighter CNN framework is proposed to enhance with two key components. Firstly, an Elephant Herding Optimization (EHO) algorithm selects pertinent features for classification tasks. The classification module integrates a Hessian-based Optimal Brain Surgeon (HOBS) approach with a pruned Extreme Learning Machine (ELM), optimizing network parameters while reducing computational complexity. The proposed pruned model gives an accuracy of 95.73%, Cohen’s kappa of 0.81%, training time of 2.35sec on Plant Village dataset, comprising 8,000 leaf images across 10 distinct classes of tomato plant, which demonstrates that this framework effectively reduces the model’s size of 9.2Mb and parameters by reducing irrelevant connections in the classification layer. The proposed classifier performance was compared to existing deep learning models, the experimental results show that the pruned DenseNet achieves an accuracy of 86.64% with a model size of 10.6 MB, while GhostNet reaches an accuracy of 92.15% at 10.9 MB. CACPNET demonstrates an accuracy of 92.4% with a model size of 18.0 MB. In contrast, the proposed approach significantly outperforms these models in terms of accuracy and processing time.

On this page, you will find the latest Tomato prices in Wokha Town Market today. Know a complete report on whether Tomato prices have risen or fallen in Wokha Town in recent days.

Over the five years to 2024, revenue for the Hydroponic Crop Farming industry has grown. While overall vegetable prices have increased over the five years, industry revenue has been limited by years with significant crop price drops. However, extreme weather conditions damaged many crops for fresh field farmers, so grocery stores and farmers' markets quickly turned to hydroponic farmers to meet their demand. In particular, drought throughout many parts of the United States over the past five years harmed agricultural industries across the board, presenting an opportunity for hydroponic crop farmers. Over the past five years, revenue has grown at a CAGR of 3.2% to $961.8 million, including an expected 0.7% increase in 2024. Profit is expected to fall slightly from 4.4% of revenue in 2019 to 4.3% in 2024. Poor weather conditions are projected to increase downstream purchasers' reliability on hydroponic farmers and raise vegetable prices, raising revenue. Mexican imports will pose a small threat to the domestic industry as the country increases its hydroponic farming capabilities. Nevertheless, as consumers increasingly purchase locally grown produce, imports will not pose a substantial threat. Per capita vegetable consumption is projected to decline slightly over the next five years, limiting revenue growth and hindering profit. Over the next five years, as revenue and downstream demand continue to expand, the number of enterprises is projected to grow. Many of these new companies will focus exclusively on growing organic fruits and vegetables to help meet demand at fresh markets. Meanwhile, other operators will start farms to provide goods solely on a local basis. However, as competition increases, profit will likely decline. Overall, revenue is forecast to grow at a CAGR of 2.1% to $1.1 billion over the five years to 2029.

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Feature selection and pruning strategies comparison.

Statistical summary of models performance metrics.

Comparative analysis of proposed approach versus baseline models.

ABSTRACT: In the present study, we investigated the influence of social and environmental factors in the genetic diversity of tomato landraces in the South-Central region of Mexico. A total of 30 tomato landraces, collected in 18 villages with different ethnolinguistic affiliations, were analyzed. We reported that the genetic diversity of tomato landraces is associated with the ethnolinguistic group, weather, and soil-type present in the region studied. Our results showed that there are morphological differences between landraces grown by different ethnolinguistic groups; however, there was also evidence of morphological similarities between landraces from groups with different ethnolinguistic affiliations. Finally, different selection criteria, mainly fruit color, size and shape, plays an important role in the phenotypic divergence among landraces grown in different traditional farming systems.

इस पेज पर आपको आज Wokha Town मंडी में टमाटर के ताज़ा भाव की जानकारी मिलेगी। पिछले दिनो में Wokha Town में टमाटर में तेज़ी रही या मंदी की सारी रिपोर्ट यहाँ पर देखे।

इस पेज पर आपको आज Mokokchung Town मंडी में टमाटर के ताज़ा भाव की जानकारी मिलेगी। पिछले दिनो में Mokokchung Town में टमाटर में तेज़ी रही या मंदी की सारी रिपोर्ट यहाँ पर देखे।