Useful AI applications in agriculture: aggregation of machine learning techniques for weather forecasting and banana plant counting

Modern problems in agricultural seek methods to increase the convenience, reliability accuracy and at the same time, cost efficacy in precision farming. To exploit the global research gaps in sustainable agriculture, this thesis organizes three different works committed to provide solutions to three modern agricultural problems: reliable crop modeling, accurate crop counting and cost-effective crop health monitoring. As strategic solutions, this study proposes the use of emerging advancements on recent artificial intelligence technologies, weather forecasting, web-services, aerial imagery and digital image processing to connect the crucial cores of sustainable agriculture visions. The outputs of this work is targeted help decision makers, and specially the farmers themselves, to increase the productivity of farms for better yield, and to draw young people to the farms to meet the global hunger of 21st century with sustainability in life support and food security. The unavailability of seasonal weather data at the simulation time (often beginning of a growth season) forces decision makers in crop growth assessment to consider multiple weather scenarios, which are often generated from longtime observed data. Ironically these scenario immediately become “outdated” as soon as the season begins, because they are always different from newly observed data. In the first research of this thesis, we investigate this dilemma and in particular address three questions: determination of most successful scenario, classification of scenarios into fresh and stale, and generation of a new scenario from fresh scenarios. Algorithms to solve these questions are given as strategies for prediction games of weather generators. We also elaborate on the applications of our results in networking existing weather generation web services.The production of banana - one of the highly consumed fruits - is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In the second research of this thesis, we propose a deep learning based algorithm for detection and counting of banana plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted only 72.8% recall for our sample images of a commercial farm in Thailand. To improve this result, we use several image processing methods to enhance the vegetative properties - radiance, hue, saturation and values (HSV), and chlorophyll content of banana leaves - to generate multiple variants of aerial images. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples, to produce multiple results of detection. We apply the same algorithm on images collected from multiple flying altitudes, and merge the detection results to increase the recall to 97.6%.Unmanned Aerial Vehicle (UAV) photogrammetry has allowed to monitor crop growth/health and remotely estimate biomass through calculation of Vegetation Indices (VI). However, some of the indices require costly sensors, and the process of generating VI maps from UAV images also requires commercial off-the-shelf software packages. The third research of this thesis uses existing open-source tools and methods to develop an algorithm for a web-service that can create orthophotos, canopy height model and VI's for red-green-blue (RGB) images collected from UAV. We also discuss on ways to balance between the processing speed and quality of outputs, and further compare them to the outputs of an existing state-of-the-art commercial service.