Inoculation methods and agressiveness of Macrophomina phaseolina isolates in cowpea

ABSTRACT: The fungi Macrophomina phaseolina is the charcoal rot causal agent, one of the most important cowpea crop disease in semiarid regions can causes 100% yield losses. The search for resistant genotypes requires efficient phenotyping. In addition, there is the problem of great variation in aggressiveness between isolates. This study aimed to 1) test three methods of inoculation in semiarid conditions, and 2) to evaluate the aggressiveness of isolates of M. phaseolina. In the first experiment carried out in greenhouse, the inoculations methods were evaluated, using two cowpea lines, three inoculation methods and three pathogen isolates. On the second experiment, fifteen M. phaseolina isolates were inoculated in one cultivar to evaluate their aggressiveness. By assessing the length of the lesions and the severity of the disease using an index, we identified the toothpick inoculation method as the most efficient. Toothpick method allowed to discriminate the genotypes and the aggressiveness of the pathogen.

A Lightweight Powdery Mildew Disease Evaluation Model for Its In-Field Detection with Portable Instrumentation

Powdery mildew is a common crop disease and is one of the main diseases of cucumber in the middle and late stages of growth. Powdery mildew causes the plant leaves to lose their photosynthetic function and reduces crop yield. The segmentation of powdery mildew spot areas on plant leaves is the key to disease detection and severity evaluation. Considering the convenience for identification of powdery mildew in the field environment or for quantitative analysis in the lab, establishing a lightweight model for portable equipment is essential. In this study, the plant-leaf disease-area segmentation model was deliberately designed to make it meet the need for portability, such as deployment in a smartphone or a tablet with a constrained computational performance and memory size. First, we proposed a super-pixel clustering segmentation operation to preprocess the images to reduce the pixel-level computation. Second, in order to enhance the segmentation efficiency by leveraging the a priori knowledge, a Gaussian Mixture Model (GMM) was established to model different kinds of super-pixels in the images, namely the healthy leaf super pixel, the infected leaf super pixel, and the cluttered background. Subsequently, an Expectation–Maximization (EM) algorithm was adopted to optimize the computational efficiency. Third, in order to eliminate the effect of under-segmentation caused by the aforementioned clustering method, pixel-level expansion was used to describe and embody the nature of leaf mildew distribution and therefore improve the segmentation accuracy. Finally, a lightweight powdery-mildew-spot-area-segmentation software was integrated to realize a pixel-level segmentation of powdery mildew spot, and we developed a mobile powdery-mildew-spot-segmentation software that can run in Android devices, providing practitioners with a convenient way to analyze leaf diseases. Experiments show that the model proposed in this paper can easily run on mobile devices, as it occupies only 200 M memory when running. The model takes less than 3 s to run on a smartphone with a Cortex-A9 1.2G processor. Compared to the traditional applications, the proposed method achieves a trade-off among the powdery-mildew-area accuracy estimation, limited instrument resource occupation, and the computational latency, which meets the demand of portable automated phenotyping.

Predicting symptoms of downy mildew, powdery mildew, and gray mold diseases of grapevine through machine learning

Downy mildew, powdery mildew, and gray mold are major diseases of grapevine with a strong negative impact on fruit yield and fruit quality. These diseases are controlled by the application of chemicals, which may cause undesirable effects on the environment and on human health. Thus, monitoring and forecasting crop disease is essential to support integrated pest management (IPM) measures. In this study, two tree-based machine learning (ML) algorithms, random forest and C5.0, were compared to test their capability to predict the appearance of symptoms of grapevine diseases, considering meteorological conditions, spatial indices, the number of crop protection treatments and the frequency of monitoring days in which symptoms were recorded in the previous year. Data collected in Tuscany region (Italy), on the presence of symptoms on grapevine, from 2006 to 2017 were divided with an 80/20 proportion in training and test set, data collected in 2018 and 2019 were tested as independent years for downy mildew and powdery mildew. The frequency of symptoms in the previous year and the cumulative precipitation from April to seven days before the monitoring day were the most important variables among those considered in the analysis for predicting the occurrence of disease symptoms. The best performance in predicting the presence of symptoms of the three diseases was obtained with the algorithm C5.0 by applying (i) a technique to deal with imbalanced dataset (i.e., symptoms were detected in the minority of observations) and (ii) an optimized cut-off for predictions. The balanced accuracy achieved in the test set was 0.8 for downy mildew, 0.7 for powdery mildew and 0.9 for gray mold. The application of the models for downy mildew and powdery mildew in the two independent years (2018 and 2019) achieved a lower balanced accuracy, around 0.7 for both the diseases. Machine learning models were able to select the best predictors and to unravel the complex relationships among geographic indices, bioclimatic indices, protection treatments and the frequency of symptoms in the previous year.

A Review on Rice Crop Disease Classification Using Computational Approach

Rice is a significant cereal crop across the world. In rice cultivation, different types of sowing methods are followed, and thus bring in issues regarding sampling collection. Climate, soil, water level, and a diversified variety of crop seeds (hybrid and traditional varieties) and the period of growth are some of the challenges. This survey mainly focuses on rice crop diseases which affect the parts namely leaves, stems, roots, and spikelet; it mainly focuses on leaf-based diseases. Existing methods for diagnosing leaf disease include statistical approaches, data mining, image processing, machine learning, and deep learning techniques. This review mainly addresses diseases of the rice crop, a framework to diagnose rice crop diseases, and computational approaches in Image Processing, Machine Learning, Deep Learning, and Convolutional Neural Networks. Based on performance indicators, interpretations were made for the following algorithms namely support vector machine (SVM), convolutional neural network (CNN), backpropagational neural network (BPNN), and feedforward neural network (FFNN).

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++

Northern leaf blight (NLB) is a serious disease in maize which leads to significant yield losses. Automatic and accurate methods of quantifying disease are crucial for disease identification and quantitative assessment of severity. Leaf images collected with natural backgrounds pose a great challenge to the segmentation of disease lesions. To address these problems, we propose an image segmentation method based on YOLACT++ with an attention module for segmenting disease lesions of maize leaves in natural conditions in order to improve the accuracy and real-time ability of lesion segmentation. The attention module is equipped on the output of the ResNet-101 backbone and the output of the FPN. The experimental results demonstrate that the proposed method improves segmentation accuracy compared with the state-of-the-art disease lesion-segmentation methods. The proposed method achieved 98.71% maize leaf lesion segmentation precision, a comprehensive evaluation index of 98.36%, and a mean Intersection over Union of 84.91%; the average processing time of a single image was about 31.5 ms. The results show that the proposed method allows for the automatic and accurate quantitative assessment of crop disease severity in natural conditions.

Multiple functions of SWI/SNF chromatin remodeling complex in plant-pathogen interactions

The SWI/SNF chromatin remodeling complex utilizes the energy of ATP hydrolysis to facilitate chromatin access and plays essential roles in DNA-based events. Studies in animals, plants and fungi have uncovered sophisticated regulatory mechanisms of this complex that govern development and various stress responses. In this review, we summarize the composition of SWI/SNF complex in eukaryotes and discuss multiple functions of the SWI/SNF complex in regulating gene transcription, mRNA splicing, and DNA damage response. Our review further highlights the importance of SWI/SNF complex in regulating plant immunity responses and fungal pathogenesis. Finally, the potentials in exploiting chromatin remodeling for management of crop disease are presented.

A Wide Survey on Data Mining Approach for Crop Diseases Detection and Prevention

India is agriculture land and major revenue manufacturing sector. However, because of amendment in temporal parameters and uncertainty in climate directly have an effect on quality and amount of the assembly and maintenance of crops. Also, quality even a lot of degrade once the crops area unit infected by any malady. The main focus of this analysis in agriculture is to increment the crop quality and potency at lower price and gain profit as result of in India the majority of the population depends on agriculture. Big selection of fruits is growing up in India such as apple, banana, guava, grape, mango, pomegranate, orange is the main one. Fruit production gives around 20% of the country’s development. However, because of absence of maintenance, inappropriate development of fruits and manual investigation there has been scale back in generate the standard of fruits.So, Data Mining Approach used in the agriculture domain to resolve several agricultural issues of classification or prediction. During this paper complete survey of several data mining approach for crop disease management has been done. Detection of disease in early state will improve in quality of crop still as decrease the production cost. Also, we can improve the production of the particular crop. Several major parameters are used for the crop disease classification or prediction.