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Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.The pathogen starts infecting roots during early soybean growth stages [9,10] and causes root rot and poor root development [3]. Root infections are favored by cool, wet soil environments [11,12]. Foliar symptoms of interveinal chlorosis (yellowing between leaf veins) and necrosis (tissue browning following cell death) typically appear during reproductive stages [13] and cause premature defoliation and senescence under severe disease pressure [14]. The initial foliar symptoms show only as yellow traces on lower leaves, which makes the disease difficult to detect at early stages. Abundant soil moisture favors SDS foliar symptom expression [12,15], whereas infected plants may not develop foliar symptoms under dry field conditions. Disease distribution within a field is limited by the spatial distribution of the pathogen at the beginning of the growing season.Scouting for SDS foliar symptoms in the field is made difficult by the relatively late onset of visible foliar symptom expression, which often occurs after the soybean canopy has closed, and by the patchy distribution of SDS in soybean fields. Scouting for symptomatic plants is time-consuming, and confirmation of Fv infection requires destructive sampling [16]. Therefore, a more effective method for monitoring and quantifying the distribution of SDS in the field is needed.Early detection of plant diseases through remote sensing can be di...

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One of the main reasons behind reduced cane yield is pathetic method of planting. Planting method and row spacing are the most important yield contributing factors in sugarcane. A field experiment was carried out in order to determine quality and yield of sugarcane in various spatial arrangements. Treatments are 180 cm spaced trenches with triple row strips; 180 cm spaced trenches with alternate row strips; 120 cm spaced trenches with double row strips and 60 cm spaced furrow with single row. Perusal of data revealed that 3.6%, 13.4%, 15%, 15.3% more cane diameter (cm), cane length (cm), stripped cane yield (t•ha −1), sugar yield (t•ha −1) were obtained from 180 cm spaced trenches with triple row strips as compared to conventional planting method i.e. 60 cm spaced furrows. While the number of millable canes m −2 , polarity %, cane juice purity %, cane juice %, commercial cane sugar % and cane sugar recovery % remained non-significant by different planting techniques.

Fruit flies are responsible for causing significant yield losses, dropping the values and creating hindrances in the exports of agricultural produces (Sarwar, 2015). Fruit flies are transported across borders with international trade of fruits and vegetables and hence are regarded as major quarantine pests (Permalloo, 1998; Peck and McQuate, 2004). The damages inflicted by the fruit flies lead to reduced farm produce and hence to limited exports Abstract | The tephritid fruit flies are the major pests of horticultural crops across the globe. In Pakistan, two fruit fly species, Bactrocera zonata (Saunders) and B. dorsalis (Hendel) cause severe qualitative and quantitative damages to various fruits. The present study was executed to record the population dynamics of these fruit fly species in guava and mango orchards with respect to meteorological factors using methyl eugenol-baited traps. The results revealed that population of both the species highly fluctuated round the year. B. zonata appeared to be the most abundant species both in mango and guava orchards as compared to B. dorsalis. The highest mean number of B. zonata (3690.57 flies/trap) was captured in August 2018 in guava orchard. From October, 2018 onward up to February 2019, population of B. zonata tended to decline with the lowest catches (122.5 and 152.8 flies/trap, respectively) in January and February, 2019. In mango orchard, peak population of B. zonata (4062.8 flies/trap) was recorded in May, 2019. Abundance of B. dorsalis in guava orchard reached to its peak (394.625 flies/trap) in August, 2018. However, in mango orchard, an increasing trend in population was observed from April onward with the highest catches of 521.4 flies/trap in June. The correlation matrix revealed a significantly positive relation among the incidence of B. zonata and minimum and maximum temperatures and sunshine hours whereas relative humidity (R.H.) and rainfall were found to have a negative correlation with B. zonata abundance. Correlation analysis of B. dorsalis catches with respect to meteorological data revealed a significantly positive correlation of monthly captured flies with all the climatic factors such as maximum temperature, minimum temperature, R.H. and sunshine duration except the mean monthly rainfall.

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In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.