DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository: https://github.com/p2irc/deepwheat_WACV-2018

Deep learning in agriculture: A survey

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.info:eu-repo/semantics/acceptedVersio

Deep learning in agriculture: A survey

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques

Applications of Image Processing in Viticulture: A Review

The production of high quality grapes for wine making is challenging. Significant progress has been made in the automated prediction of harvest yields from images but the analysis of images to predict the quality of the harvest has yet to be fully addressed. The quality of wine produced depends in part on the quality of the grapes harvested and therefore on the presence of disease in the vineyard. There is potential for automated early detection of disease in grape crops through the development of accurate techniques for image processing. This paper presents a review of current research and highlights some of the key challenges for geo-computation (image processing, computer vision and data mining techniques) to inform the management of vineyards and highlights the key challenges for in-field image capture and analysis. An exploration of potential applications for the knowledge generated by imaging techniques is then presented. This discussion is driven by the current interest in the effect of rapid and dramatic climate change on the production of wine and focuses on how this information might be utilized to inform the design and validation of accurate predictive models

DETECTION OF PLANT LEAF DISEASES IN AGRICULTURE USING RECENT IMAGE PROCESSING TECHNIQUES – A REVIEW

Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture. Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops. Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture

Crown-level mapping of tree species and health from remote sensing of rural and urban forests

Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms are now available to apply these new data sources toward the discrimination and the mapping of tree species and health classes. The dissertation includes an introductory chapter, three stand-alone manuscripts, and a concluding chapter, each of which support the overarching theme of mapping tree species composition and health using remote sensing images. The first manuscript, now published in the International Journal of Remote Sensing, confirms the utility of combining VHR multi-temporal satellite data with LiDAR datasets for tree species classification using machine learning classifiers at the crown level in a rural forest the Fernow Experimental Forest, West Virginia. This research also evaluates the contribution of each type of spectral, phenological and structural feature for discriminating four tree species: red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina). The second manuscript investigates the performance of tree species classification in urban settings with three contributions: 1) 12 very high resolution WorldView-3 images (WV-3), whose image acquisition date covering the growing season from April to November; 2) a large forest inventory providing sufficient calibration/validation datasets in Washington D.C.; 3) object-based tree species classification using the RandomForest machine learning algorithm. This manuscript identifies the incremental losses in classification accuracy caused by iteratively expanding the classification to 19 species and 10 genera. It also identifies the optimum pheno-phases and spectral bands for discriminating trees species in urban settings. Building on these promising results from the second manuscript, the third manuscript detect a signal of statistical difference among individual tree health conditions using WorldView-3 images from June 11th, July 30th and August 30th , 2017 in Washington D.C.. It examines six vegetation indices calculated from WorldView-3 images to describe three health condition levels in good, fair and poor, and discusses the effects of green-down phenology for tree health analysis. Overall, this dissertation research contributes to remote sensing research by combining data from both active and passive sensors to discriminate tree species in rural forest. For the species-rich urban settings, this dissertation illustrates the importance of phenology for tree species classification at crown level using VHR remote sensing images. Finally, this dissertation provides important insights on detecting statistical differences among tree health conditions at individual crown-level in the urban environment using VHR remote sensing images

Phenology satellite experiment

There are no author-identified significant results in this report

Extracting flowering phenology from grassland species mixtures using time-lapse cameras

Understanding the impacts of climate change on plant phenology is crucial for predicting ecosystem responses. However, accurately tracking the flowering phenology of individual plant species in grassland species mixtures is challenging, hindering our ability to study the impacts of biotic and abiotic factors on plant reproduction and plant-pollinator interactions. Here, we present a workflow for extracting flowering phenology from grassland species mixtures using near-surface time-lapse cameras. We used 89 image series acquired in plots with known species composition at the Jena trait-based experiment (Germany) to develop random forest classifiers, which were used to classify images and compute time series of flower cover for each species. The high temporal resolution of time-lapse cameras allowed to select images in proper light conditions, and to extract vegetation indices and texture metrics to improve discrimination among flowering species. The random forest classifiers showed a high accuracy in predicting the cover of Leucanthemum vulgare, Ranunculus acris, and Knautia arvensis flowers, whereas graminoid flowers were harder to predict due to their green-to-brownish colours. The proposed workflow can be applied in climate change studies, ecosystem functioning, plant community ecology, and biodiversity change research, including the investigation of effects of species richness on individual species' flowering phenology. Our method could be a valuable tool for understanding the impacts of climate change on plant reproduction and ecosystem dynamic

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

Hybrid features and ensembles of convolution neural networks for weed detection

Weeds compete with plants for sunlight, nutrients and water. Conventional weed management involves spraying of herbicides to the entire crop which increases the cost of cultivation, decreasing the quality of the crop, in turn affecting human health. Precise automatic spraying of the herbicides on weeds has been in research and use. This paper discusses automatic weed detection using hybrid features which is generated by extracting the deep features from convolutional neural network (CNN) along with the texture and color features. The color and texture features are extracted by color moments, gray level co-occurrence matrix (GLCM) and Gabor wavelet transform. The proposed hybrid features are classified by Bayesian optimized support vector machine (BO-SVM) classifier. The experimental results read that the proposed hybrid features yield a maximum accuracy of 95.83%, higher precision, sensitivity and F-score. A performance analysis of the proposed hybrid features with BO-SVM classifier in terms of the evaluation parameters is made using the images from crop weed field image dataset