Abstract
The abstract highlights the widespread application of Deep Learning (DL) in agriculture, specifically mentioning Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). It emphasizes the need for researchers in agriculture to understand these DL algorithms to enhance data analysis and research outcomes in the field. The article aims to provide a concise overview of major DL techniques, their implementation, limitations, and training processes, to support quick learning and effective utilization of DL in agriculture.
Introduction
The introduction explains the structure of a standard artificial neural network (ANN), which consists of interconnected neurons that process real-valued activations. When environmental changes are detected by sensors, input neurons are activated, leading to the activation of other neurons through weighted connections. Deep learning (DL) focuses on accurately assigning credit across multiple computational stages in neural networks to enhance data analysis and improve research outcomes in various fields, including agriculture.
Common deep learning
Common deep learning algorithms include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). These algorithms are widely used in various applications. Other sub-category DL algorithms, such as VGGNet, ConvNets, LSTM, and DCGAN, can be derived from these common DL algorithms directly or indirectly, showcasing the versatility and adaptability of deep learning techniques in solving complex problems.
Convolutional neural network
Convolutional Neural Networks (CNN) are a type of deep learning algorithm that consists of convolutional layers, pooling layers, and fully connected layers. CNNs have been successful in various applications such as speech recognition, face recognition, and natural language processing by extracting features through convolution and pooling layers and then classifying them using fully connected layers. Compared to traditional neural networks, CNNs are effective for tasks like image classification due to their ability to handle high-dimensional arrays and reduce the number of parameters through mechanisms like local perception and parameter sharing.
Deep learning framework
Deep learning frameworks, such as Tensor Flow and Caffe, are essential tools that enable users to implement deep learning algorithms without extensive programming knowledge. Tensor Flow, developed by Google, supports various deep learning models like CNN, RNN, and GAN, offering flexibility, multi-language support, and a rich algorithm library. Caffe, on the other hand, is known for its speed in handling massive data, modularity for extending to new tasks, and providing a clean framework for deploying convolutional neural networks efficiently.
Applications in agriculture
Recent applications of Deep Learning (DL) in smart agriculture involve using techniques like Convolutional Neural Networks (CNN) for tasks such as plant disease detection, plant classification, and weed identification. DL models have shown impressive performance in automating these agricultural processes by analyzing images of plants and leaves to detect diseases, classify plants, and identify weeds with high accuracy. These applications help in improving crop management, pest control, and overall agricultural productivity through the use of advanced DL technology.
Discussion
The recent advances in agriculture are closely tied to enhancing crop productivity, addressing plant diseases, and advancing mechanized and automated farming practices. Deep learning (DL) is commonly applied in agriculture for tasks like image recognition and data classification, involving steps such as data collection, neural network training, model testing, and result analysis. By combining DL with technologies like UAVs and IoT, high-quality datasets are generated, leading to improved accuracy in agricultural applications and the development of more effective tools.