5 Ways to Apply Data Science to Agriculture for Better Agronomy

Data Science can help farmers collect, analyze, and use data from various sources

Data science is a branch of study that draws knowledge and insights from data using scientific methods, algorithms, and systems. Data Science can help agronomists collect, analyze, and use data from various sources, such as soil, weather, crops, and markets, to make better decisions and optimize their operations. Data Science can also help agronomists discover new patterns and relationships that may otherwise be hidden or unknown.

1. Image Classifier for Plant Species Identification: The objective of this data science project is to precisely identify 99 plant species using binary leaf photos and extracted characteristics including form, border, and texture. The efficiency of classifiers in image classification applications will be evaluated using a variety of classification algorithms. With the aid of this project, you will learn which Python libraries, such as Scipy, Sklearn, and TensorFlow, are most appropriate for the dataset files to build a successful system for detecting plant species.

2. Crop Mask using R-CNN: This project seeks to develop and apply instance segmentation methods for mapping irrigated center-pivot agriculture using multispectral satellite images. The main goal is to develop a model or group that can precisely and accurately map center-pivot agriculture across various dryland agriculture zones. Before fine-tuning Landsat tiles from many cloud-free images shot in Nebraska during the growth season of 2005, the project's main methodology incorporates transfer learning.

3. Smart Agriculture System: The analysis of data pertaining to soil conditions, such as moisture content, temperature, and chemical composition, all of which have an impact on crop growth and cattle welfare, is best served by data science. In order to find crop illnesses and weed infestations, this data science research evaluates the yields of diverse plant types. Exploratory data analysis is the initial step in this project, and HeatMap is used to check the dataset for null or missing values.

4. Crop Monitoring: One of the most important applications of Data Science in agriculture is crop monitoring. Crop monitoring involves collecting and analyzing data about the status and performance of crops throughout their growth cycle. This data can help agronomists identify problems early on and take corrective actions to improve yields and quality.

5. Plant Disease Prediction: Machine learning is especially useful for identifying and detecting plant illnesses early on by analyzing the diseases' signs.  The destruction of leaves is caused by bacteria, fungi, viruses, and other insects. The project employs a Support Vector Machine algorithm to classify tree leaves, identify the illness, and provide fertilizer. By using the Support Vector Machine (SVM) approach, the leaf picture is separated into categories for normal and impacted conditions