# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 3 IoT ML Models
### Neural Networks and Deep Learning in Agriculture  
The agriculture sector is one of the most critical areas where the use of artificial intelligence can be highly beneficial. Machine learning is used to solve a variety of problems within the agricultural sector, such as agricultural productivity, resource management, and decision-making processes [25; 26].  
Deep learning in agriculture refers to using advanced artificial intelligence (AI) techniques, specifically deep neural networks, to analyze and interpret complex agricultural data for better decision making, farming process optimization, and addressing agricultural challenges. Deep learning is the automatic extraction of detailed patterns and representations from big and diverse datasets using deep neural networks with numerous layers.  
Deep learning models are developed on neural network topologies, often deep neural networks with several layers. In agricultural applications, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) are often used. CNNs are increasingly used in agriculture for weed detection, crop pest classification, and plant disease diagnosis [27].  
Weed management is one of the major problems with crop management [28]. Since both weeds and crops have similar colors, shapes, and textures throughout the growth phase, weed detection in crops through imaging is a challenging problem. Deep learning algorithms are used to detect and classify weeds. According to [29], supervised learning algorithms are the most used in the literature, and they achieve high classification accuracy by fine-tuning pre-trained models on any plant dataset when a large amount of labeled data are available.  
In the agriculture field, pest recognition is a difficult task for farmers, and it is a critical issue in yield production. Identifying and classifying all types of crop insects is the most difficult task for farmers due to their similar appearance in crop growth. In [30], a CNN is used to tackle this issue as it automatically extracts features and learns high-level features in picture classification applications.  
Deep learning can be used in agriculture to monitor crop health, detect illnesses, and evaluate general field conditions by analyzing photos from remote sensing technology [31]. The authors in [32] investigate the combination of deep learning algorithms and multi-temporal images. They analyze and compare crop classification applications based on deep learning models and various time-series data to increase crop classification accuracy.  
Based on estimates of crop yields, weather patterns, and other factors, deep learning is used in predictive modeling to enable farmers to make well-informed decisions. The authors in [33] use deep neural networks to predict yield corn hybrids from genotype and environment data. In [34], two deep learning models, long short-term memory and gated recurrent units, are used to analyze agricultural datasets and to predict end-of-season yields.  
Because of its impact on various aspects of the industry, deep learning is important in the agricultural field. The ability of deep learning models to process and analyze complex and large datasets allows for more accurate and efficient decision making in farming practices. Deep learning plays a crucial role in crop management by enabling precise monitoring, identification of crops, and early detection of diseases or pests. Deep learning promotes sustainability by minimizing waste and reducing environmental impact.