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Models Keywords (407)
Agriculture
10 models found.
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Abalone is common name for any group of small to very large sea snails, commonly found along the coasts across the world,... moreAbalone is common name for any group of small to very large sea snails, commonly found along the coasts across the world, and used as delicacy in cuisines and it's leftover shell is fashioned into jewellery due to it's iridescent luster. Due to it's demand and economic value it's often harvested in farms, and as such the need to predict the age of abalone from physical measurements. Traditional approach to determine it's age is by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task.Dataset Link: https://www.kaggle.com/hurshd0/abalone-uci less
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Cacao (Theobroma cacao), popularly known as Cocoa, is a tropical evergreen tree in the Malvaceae family farmed for its... moreCacao (Theobroma cacao), popularly known as Cocoa, is a tropical evergreen tree in the Malvaceae family farmed for its delicious seeds.The Project aims to detect whether cacao is healthy or diseased.The dataset consists of '2,092' Cocoa images of size '1080x1080'. The dataset includes classes 'Healthy' and 'Unhealthy,' having 1,046 images in each of them.The "Accuracy_score" metric has been used to measure that model's performance.
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With the enhancement in agricultural technology and the use of artificial intelligence in diagnosing plant diseases, it... moreWith the enhancement in agricultural technology and the use of artificial intelligence in diagnosing plant diseases, it becomes important to make pertinent research to sustainable agricultural development. Various diseases like early blight and late blight immensely influence the quality and quantity of the potatoes and manual interpretation of these leaf diseases is quite time-taking and cumbersome. As it requires tremendously a good level of expertise, efficient and automated detection of these diseases in the budding phase can assist in ameliorating the potato crop production. Previously, various models have been proposed to detect several plant diseases. In a model is presented that uses convolution nueral network to extract the relevant features from the dataset. Then, with the help of multiple classifiers results with accuracy obtaining 95% over the test dataset.Datset Features :class 0 :Potato___Early_blightclass 1 :Potato___Late_blightclass 2 :Potato___healthyDataset Link : https://www.kaggle.com/sohaibalam67/potato-diseaseAccuracy of Model : 95%Classification Report : less
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Rice is a significant agricultural crop. On the other hand, Crop diseases may drastically lower output and quality, posing a... moreRice is a significant agricultural crop. On the other hand, Crop diseases may drastically lower output and quality, posing a danger to global food supplies.
The Project’s aim is to detect whether a rice leaf is ‘healthy’ or is having ‘Bacterial leaf blight’, ’Brown spot’ or ’Leaf smut’ disease.
The dataset contains 16,000 images of disease-infected rice leaves. The images are grouped into 4 classes based on the type of disease. There are 4,000 images in each class.
The "Accuracy_score" metric has been used to measure the model's performance. less
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November 27, 2021 - MODEL_posted_by
Prasad Chaskar,
659 views, 2 likes
Crop prediction is an essential task for the decision-makers at national and regional levels for rapid decision-making. An... moreCrop prediction is an essential task for the decision-makers at national and regional levels for rapid decision-making. An accurate crop yield prediction model can help farmers to decide on what to grow and when to grow.The dataset contains following 21 crops :['rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas', 'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate', 'banana', 'mango', 'grapes', 'watermelon', 'muskmelon', 'apple', 'orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee']
crop_labels = {0:'apple',1:'banana',2:'blackgram',3:'chickpea',4:'coconut',5:'coffee',6:'cotton',7:'grapes',8:'jute',9:'kidneybeans',
9:'lentil',10:'maize',12:'mango',13:'mothbeans',14:'mungbean',15:'muskmelon',16:'orange',17:'papaya',18:'pigeonpeas',19:'pomegranate',20:'rice',21:'watermelon'}
Dataset Link : https://www.kaggle.com/atharvaingle/crop-recommendation-datasetAccuracy of Model : 98 %Classification Report for Model : less
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Pisciculture or Fish culture is the breeding and rearing of fishes in ponds, reservoirs (dams), lakes, rivers, and paddy... morePisciculture or Fish culture is the breeding and rearing of fishes in ponds, reservoirs (dams), lakes, rivers, and paddy fields.
Pisciculture helps in integrated rural development by generating employment and income for the fishing community and fish farmers.
The dataset includes gilt head bream, red sea bream, sea bass, red mullet, horse mackerel, black sea sprat, striped red mullet, trout, shrimp image samples. The dataset includes a total of '9,000' images, i.e.,' 1000' images per class.
The project aims to detect seafood that belongs to 9 different aquatic species.
The "Accuracy_score" metric has been used to measure that model's performance. less
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Mushroom poisonings can occur because of forager misidentification of a poisonous species as edible, although many cases are... moreMushroom poisonings can occur because of forager misidentification of a poisonous species as edible, although many cases are intentional ingestions. The project can identify 'Toxic mushrooms' and 'Edible mushrooms' to prevent humans from consuming them and falling sick. Likewise, organizations can use the project to weed out 'Poisonous Mushrooms' during production or harvesting. The dataset includes hypothetical samples corresponding to 23 gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). The "Accuracy_score" metric has been used to measure that model's performance. less
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Corn is one of India's most popular food grains, and crop loss due to diseases substantially affects the Indian economy and... moreCorn is one of India's most popular food grains, and crop loss due to diseases substantially affects the Indian economy and threatens food availability.
The dataset includes 4,226 images belonging to 2 classes, i.e., 'Healthy corn' and 'Infected.'
The project will help Agriculture sectors for making systems that can help solve farmers' problems using Artificial Intelligence.
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August 6, 2021 - MODEL_posted_by
Aarzoo Goel,
468 views, 2 likes
Dandelions (Binary Image Classification)
These are images of dandelions and not-dandelions( grass or other). The goal of... moreDandelions (Binary Image Classification)
These are images of dandelions and not-dandelions( grass or other). The goal of this project is a very simple binary image classification model for me to do some "real-world learning". Initial thoughts and findings: Need lots and lots more images. While the training results in decent accuracy, the validation loss is substantial. My initial 1,200+ images (50% dandelion/50% not) seems woefully small.
Dataset: https://www.kaggle.com/coloradokb/dandelionimagesDandelions: 635Other: 627
ModelMobileNetV2
AccuracyValidation Accuracy: 84% (We will take this as approximate accuracy and model can be more accurate in further studies with more data.) less
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July 5, 2021 - MODEL_posted_by
Tarun Reddy,
921 views, 3 likes
PROBLEM STATEMENT:
Agriculture is one wide sector and that plays a very important role for our county. While keeping track... morePROBLEM STATEMENT:
Agriculture is one wide sector and that plays a very important role for our county. While keeping track of diseases in plants with the help of specialists can be very costly in the agricultural regions. There is a need for a system that can automatically detect the diseases as it can bring revolution in monitoring large fields of crop and then plant leaves can be taken cure as soon as possible after detection of disease. There are so many diseases that affect cotton and many more crops that affect many filed of agriculture. So those identify this disease and how to recover from it. This objective will satisfy through our application which helps with do prediction of cotton disease as well as how to overcome it.DATA DESCRIPTION:The dataset which we used in this model contains 2325 images of the diseased cotton leaf, fresh cotton leaf, diseased cotton plant, and fresh cotton plant.source: https://www.kaggle.com/janmejaybhoi/cotton-disease-datasetACCURACY:Accuracy: 92%Val Accuracy: 86% less
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