CREATING A NEURAL NETWORK SERVICE FOR IDENTIFYING PROBLEM AREAS OF AGRICULTURAL FIELDS

There is a huge problem in creating space today because of growing population and research is going on profusely in finding space to dump waste. The waste has been dumped to rivers, underground and mixed with soil and by other methods. But all these methods are harmful to environment in long term. Our research is done on finding efficient way to segregate waste followed by recycling of wastes. The difficulties in isolation of various products are dealt using machine learning approach. The framework used to robotize the procedure of waste isolation to deal with the junk effectively and productively is one of the Machine Learning strategies called Convolutional Neural Network (CNN). The experiments showed that the performance of CNN is better because it recognizes the components in an image and recombines these components to recognize other structures while other methods learn to recognize as they go through it. The work will be segregated into 6 bins consisting of biodegradable, non- biodegradable. Here we have used the TensorFlow algorithm which uses Python. The applications of TensorFlow are Python application itself. The application of our research includes waste segregation in society, in industries, in agricultural fields. The recycled wastes can be used as organic material in many places

Download Full-text

Gradient convolutional neural network for classification of agricultural fields with contour levee

International Journal of Remote Sensing ◽

10.1080/01431161.2021.2003467 ◽

2021 ◽

Vol 43 (1) ◽

pp. 75-94

Author(s):

Abolfazl Meyarian ◽

Xiaohui Yuan ◽

Lu Liang ◽

Wencheng Wang ◽

Lichuan Gu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Agricultural Fields

Download Full-text

Leaf Disease Classification Using Convolutional Neural Network

10.21467/proceedings.114.4 ◽

2021 ◽

Author(s):

Mitali V. Shewale ◽

Rohin Daruwala

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Agricultural Products ◽

Disease Classification ◽

Agricultural Fields ◽

Human Intervention ◽

Feasible Solutions ◽

Major Domain

Agriculture is a major domain that contributes a lot for building up the country’s Economy; contributing to the GDP area synthesis of 17.9%. India stands second in production of agricultural products. Promising technologies such as Internet of Things, Machine Learning, Deep learning, Artificial neural networks contributes towards the most effective and reliable solutions by providing the most feasible solutions in making of different domain modernization through automation in monitoring and maintenance of agricultural fields with minimum human intervention. This paper presents a convolutional neural network based customized VGG framework and a lightweight architecture for the classification of tomato leaves affected with various diseases. Experimental analysis is performed on publically available PlantVillage dataset. After rigorous experiment we fined tuned the CNN model to obtain mAP of 83.33%.

Download Full-text

Weeds Detection in Agricultural Fields using Convolutional Neural Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1327.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 292-296

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Convolutional Neural Network ◽

Image Classification ◽

Weed Control ◽

Classification System ◽

Agricultural Fields ◽

Use Of Technology ◽

Neural Network Algorithm

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weeds herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control robot using the Lego Mindstorm EV3 which connected to a computer. The robot consists of motors, servo motors and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. The robot will spray the weed herbicides directly to the area that have been detected weeds near or at it. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Therefore, by the use of technology especially in artificial intelligence, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

Download Full-text