Deep learning-based multi-spectral identification of grey mould
نویسندگان
چکیده
Early detection of economically important plant diseases, such as grey mould caused by Botrytis cinerea, is major importance for the timely application disease management strategies and reduction impacts on crop production environment. In this study, artificial inoculation leaves cucumber plants with B. cinerea under controlled environment was performed. Multi-spectral imaging used to capture fungal spectrum response at 460, 540, 640, 700, 775 875 nm, laveraging both RGB Near Infrared (NIR) channels. Two annotated image datasets were created from collected multi-spectral images named Botrytis-detection Botrytis-classification. Several deep learning-based classification object experiments conducted datasets. Classification results indicated that learning models can separate two classes accuracy 0.93 (F1-score 0.89), while achieved a mean average precision (mAP50) 0.88, paving way future early cinerea.
منابع مشابه
Deep Spectral Clustering Learning
Clustering is the task of grouping a set of examples so that similar examples are grouped into the same cluster while dissimilar examples are in different clusters. The quality of a clustering depends on two problem-dependent factors which are i) the chosen similarity metric and ii) the data representation. Supervised clustering approaches, which exploit labeled partitioned datasets have thus b...
متن کاملDeep Multi-Spectral Registration Using Invariant Descriptor Learning
In this paper, we introduce a novel deep-learning method to align cross-spectral images. Our approach relies on a learned descriptor which is invariant to different spectra. Multi-modal images of the same scene capture different signals and therefore their registration is challenging and it is not solved by classic approaches. To that end, we developed a feature-based approach that solves the v...
متن کاملCrop Land Change Monitoring Based on Deep Learning Algorithm Using Multi-temporal Hyperspectral Images
Change detection is done with the purpose of analyzing two or more images of a region that has been obtained at different times which is Generally one of the most important applications of satellite imagery is urban development, environmental inspection, agricultural monitoring, hazard assessment, and natural disaster. The purpose of using deep learning algorithms, in particular, convolutional ...
متن کاملDeep Spectral Clustering Learning - final_version
Clustering is the task of grouping a set of examples so that similar examples are grouped into the same cluster while dissimilar examples are in different clusters. The quality of a clustering depends on two problem-dependent factors which are i) the chosen similarity metric and ii) the data representation. Supervised clustering approaches, which exploit labeled partitioned datasets have thus b...
متن کاملOperation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm
: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Smart agricultural technology
سال: 2023
ISSN: ['2772-3755']
DOI: https://doi.org/10.1016/j.atech.2023.100174