# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 5 Experimental Results  
This section includes data collection, description, classification, and discussion of the results of the proposed method. Predicting and classifying agricultural data involves utilizing machine learning and data analysis techniques to make forecasts or classify information. In this research work, three types of agricultural data are used in crop forecasting, disease recognition, and plant identification. Larger and more diverse datasets enable the proposed model to learn patterns and relationships more comprehensively using machine learning. Data cleaning, feature extraction, and supervised classification methods are applied to three types of agriculture data to achieve optimal prediction and classification to log their results. The agriculture datasets used in this research is given below:  
Figure 3: General structure of proposed model. * The crop-rec [35] dataset contains pH, rainfall, humidity, temperature, potassium, phosphorus, and soil nitrogen level as data features for optimum crop growth recommendations. The dataset has 22 crop yield classes: apple, banana, rice, cotton, maize, lentil, and papaya. The dataset has 2200 records and eight attributes. The feature characteristics of the crop-rec dataset are as follows:
* The values range from 0 to 140 for nitrogen, 5 to 145 for phosphorus, and 5 to 205 for potassium. * Temperature ranges from about 8.83 to 43.68. * Humidity ranges from about 14.26% to 99.98%. * pH ranges from 3.50 to 9.94, indicating a wide range of soil acidity and alkalinity. * Rainfall ranges from about 20.21 mm to 298.56 mm. * The pla-spe [36] dataset contains information on 100 plant species' leaves for classification. Features are extracted from images and created using an expert recommendation system. There are 1600 records with leaf information based on texture, margin, and shape. * The soy-dis [37] dataset contains 35 features and 19 classes for large soybean diseases. The features and classes are given in the table below. Table 1 shows detailed information for the soy-dis dataset. \begin{table}
\begin{tabular}{c c} \hline
**Feature** & **Set** \\ \hline Month & April, May, June, July, August, September, October, N/A \\ \hline Plant Condition & normal, lt-normal, N/A \\ \hline Precip & lt-normal, normal, gt-normal, N/A \\ \hline Temperature & lt-normal, normal, gt-normal, N/A \\ \hline Hail & yes, no, N/A \\ \hline Crop-history & diff 1 st year, same 1 st year, same 1 st two years, same 1 st several years, N/A \\ \hline area damaged & scattered, low areas, upper areas, whole field, N/A \\ \hline severity & minor, pot-severe, severe, N/A \\ \hline seed-tmt & none, fungicide, other, N/A \\ \hline germination & 90–100\%, 80–89\%, 0–80\%, N/A \\ \hline plant-growth & normal, abnormal, N/A \\ \hline leaves & normal, abnormal \\ \hline spots halo & absent, yellow-halos, no-yellow-halos, N/A \\ \hline spots marg & w-s-marg, no-w-s-marg, dna, N/A \\ \hline spot size & lt-1/8, gt-1/8, dna, N/A \\ leaf shread & no, yes, N/A \\ \hline leaf malf & no, yes, N/A \\ \hline leaf mild & no, upper surf, lower surf, N/A \\ \hline stem & normal, abnormal, N/A \\ \hline lodging & yes, no, N/A \\ \hline stem-cankers & no, below soil, above soil, above sec nde \\ \hline canker lesion & dna, brown, dk-brown-blk, tan, N/A \\ \hline fruiting bodies & no, yes, N/A \\ \hline external decay & no, firm and dry, watery, N/A \\ \hline \end{tabular}
\end{table} 
Table 1: Description of the soy-dis dataset. The soy-dis dataset has 19 classes for diseases such as charcoal rot, brown stem rot, bacterial blight, purple seed stain, and frog eye leaf spot. In this work, three agricultural datasets are used. Table 2 gives the number of samples, number of features, feature type, data distribution, and number of labels for each dataset. In this work, machine learning methodologies are applied to classify agriculture data to increase productivity and quality.