Image-based Microplot Segmentation/Detection and Deep Learning in Plant Breeding Experiments

In the coming years, the agricultural sector will encounter significant challenges from population growth, climate change, and evolving consumer demands. To address these challenges, farmers and plant breeders actively develop advanced plant varieties with enhanced productivity and resilience to harsh environmental conditions. However, the current methods for evaluating plant traits, such as manual operations and visual assessment by breeders, are time-consuming and subjective. A promising solution to this issue is image-based phenotyping, which leverages image-processing and machine-learning techniques to facilitate rapid and objective monitoring of numerous plants, enabling breeders to make more informed decisions. In order to perform per-microplot phenotypic analysis from the imagery and extract phenotypic traits from the field, it is necessary to identify and segment individual microplots (a small subdivided area within a field) in the orthomosaics. Nonetheless, the current procedures for segmenting and identifying microplots within aerial imagery used in agricultural field experiments necessitate manual operations, resulting in considerable time and labour investments. By automating this process, the evaluation of microplot phenotypes, such as physical traits, can be expedited, facilitating automated monitoring and quantification of plant characteristics. Our objective is to develop novel phenotyping algorithms to segment, detect, and classify microplots using image-processing and machine-learning techniques to achieve the goal. The thesis comprises four projects such as a comprehensive review of vegetation and microplot segmentation methods, the development of algorithms for the detection of both rectangular and non-rectangular microplots, and the utilization of deep learning techniques to predict lodging on microplots and highlighting the impact of deep learning on microplot phenotyping. These innovative approaches possess broad applicability in remote sensing field trials, encompassing diverse applications such as weed detection, crop row identification, plant recognition, height estimation, yield prediction, and lodging detection. Moreover, our proposed methods hold great potential for streamlining microplot phenotyping efforts by reducing the need for labour-intensive manual procedures

UAV Remote Sensing: An Innovative Tool for Detection and Management of Rice Diseases

Unmanned aerial vehicle (UAV) remote sensing is a new alternative to traditional diagnosis and detection of rice diseases by visual symptoms, providing quick, accurate and large coverage disease detection. UAV remote sensing offers an unprecedented spectral, spatial, and temporal resolution that can distinguish diseased plant tissue from healthy tissue based on the characteristics of disease symptoms. Research has been conducted on using RGB sensor, multispectral sensor, and hyperspectral sensor for successful detection and quantification of sheath blight (Rhizoctonia solani), using multispectral sensor to accurately detect narrow brown leaf spot (Cercospora janseana), and using infrared thermal sensor for detecting the occurrence of rice blast (Magnaporthe oryzae). UAV can also be used for aerial application, and UAV spraying has become a new means for control of rice sheath blight and other crop diseases in many countries, especially China and Japan. UAV spraying can operate at low altitudes and various speeds, making it suitable for situations where arial and ground applications are unavailable or infeasible and where precision applications are needed. Along with advances in digitalization and artificial intelligence for precision application across fertilizer, pest and crop management needs, this UAV technology will become a core tool in a farmer’s precision equipment mix in the future

Drone Based Image Processing For Precision Agriculture

In today’s world, with an advent of technological advancements, the use of automated monitoring in agriculture is gaining increase in demand. In the agricultural field, yield loss occurs primarily due to widespread disease. Most of the disease is detected and identified when the disease progresses to a severe stage. A specially equipped UAV can perform several important tasks in agriculture, including monitoring the agriculture land and perform disease detection for several plants at an early stage. Currently, disease traits in agriculture are visually assessed, which can be time-consuming, less accurate and more subjective. Hence, in this project, image processing is used for the detection of plant disease. Detection of plant disease using automated image processing method is beneficial as it can reduce huge work of monitoring in big farms comprising of numerous crops. Moreover, in order to monitor big farms it is a viable option to use unmanned aerial vehicle on specific drone (UAV) to take the snap shots of various diseased plants from multiple angles. This study proposes a parallel image segmentation algorithm in order to detect the diseased leaf in Coconut, Palm, Banana, Dwarf Palmetto and Sapodilla plants acquire using Parrot PF728000 Anafi Drone with 4K HDR Camera. At first, the parallel K-means clustering algorithm was applied on the acquired image to segregate various components acquired using UAV. Post K-means clustering, the diseased portions of the plants were assessed using Hue-Saturation-Value (HSV) based image segmentation algorithm. Moreover, a comparison for image segmentation was also done on non-K-means clustered image and K-means clustered image for which a difference of 1839

Application of image processing methodologies for fruit detection and analysis

En aquesta memòria es presenten diversos treballs d'investigació centrats en l’automatització d’operacions agrícoles mitjançant l’aplicació de diverses tècniques de processament d’imatge. En primer lloc es presenta un mètode desenvolupat per detectar i comptar raïms mitjançant la localització de pics d'intensitat en superfícies esfèriques. En segon lloc es desenvolupa un sistema de recol•lecció automàtica de fruita mitjançant la combinació d'una càmera estereoscòpica de baix cost i un braç robòtic. En tercer lloc es proposa una aplicació en què es desenvolupa un mètode basat en l'ús de la informació de color per a la verificació d'una varietat de nectarines de forma automàtica i individual en una línia d’embalatge de fruita. Finalment s’han estudiat les correlacions entre els paràmetres de qualitat de la fruita i el espectre visible de la seva pell amb l’objectiu de controlar la seva qualitat de forma no destructiva durant el seu emmagatzematge.En esta memoria se presentan diversos trabajos de investigación centrados en la automatización de operaciones agrícolas mediante la aplicación de distintas técnicas de procesado de imágenes. En primer lugar se presenta un método desarrollado para detectar y contar uvas rojas mediante la identificación de picos de intensidad en las superficies esféricas. En segundo lugar se desarrolla un sistema de recolección automática de fruta mediante la combinación de una cámara estereoscópica de bajo coste y un brazo robótico. En tercer lugar se propone una aplicación en la que se desarrolla un método de procesamiento de imágenes basado en el uso de la información de color para la verificación de una variedad de nectarinas de forma automática e individual en una línea de envasado de fruta. Finalmente se han estudiado las correlaciones entre los parámetros de calidad de la fruta y el espectro visible de su piel con el fin de controlar su calidad de forma no destructiva durante el almacenamiento.This memory introduces several research works developed to automate agricultural tasks by applying image processing techniques. In the first place a new image processing method is proposed for detecting and counting red grapes by identifying specular reflection peaks from spherical surfaces. The proposal of the second application is to develop an automatic fruit harvesting system by combining a low cost stereovision camera and a robotic arm. The third application proposed is to develop a novel image processing method based on the use of color information to verify an in-line automatic and individual nectarine variety verification in a fruitpacking line. Finally, a study focused on assessing correlations between post-storage fruit quality indices and the visible spectra of the skin of the fruit is proposed in order to control fruit quality in a non-destructive way during the storage