Remote Sensing-Based Machine Learning Applications in Agriculture

Dear Colleagues,

Agriculture is key to food security. It has the potential to improve the quality of life and livelihoods by increasing incomes and providing stable employment. However, according to the United Nations, the global population is projected to reach about 10 billion by 2050. That would demand a 50% increase in food production. The food and the nutritional demands of ballooning populations worldwide would require more precise and timely agricultural cropland products. Monitoring agriculture becomes essential in attaining food security for implementation of various agricultural serving programs. In agriculture, monitoring of crop changes, land use changes and other physical parameters helps in estimating agriculture statistics, crop yield and drought risk, which in turn helps solving problems faced by farming communities. Therefore, fast and reliable monitoring is a must. Using traditional methods, monitoring requires a large amount of satellite data downloading and processing time. Cloud computing platforms enable us to save time in downloading and processing satellite data. In recent times, machine-learning algorithms like Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) have been used by researchers to classify satellite data and their derivatives into spatial agriculture products. Additionally, usage of UAVs has been increasing to monitor the agriculture on a field scale.

This Special Issue is aimed at bringing together research reports that describe new methodologies, applications, and algorithms recently developed in monitoring agriculture. The objective of the Special Issue is to increase awareness of the studies of using various approaches and areas of research in agriculture.

Contributions are expected to deal with (but not limited to) the use of artificial intelligence, deep learning, machine learning, big data, UAVs/drones, and any of the areas described in the keywords that are related to the assessment and monitoring of agriculture.

Dr. Murali Krishna Gumma
Guest Editor

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