REMOTE SENSING TECHNOLOGIES AS A TOOL FOR COTTON LEAFWORM, SPODOPTERA LITTORALIS (BOISD.): PREDICTION OF ANNUAL GENERATIONS

The study was carried out at Menia Governorate during (2014/2015) sugar beet season under field condition. The temperature is an important environmental factor that has an effect on the rate of development, survival and in any other biological and ecological aspects for the cotton leafworm, Spodoptera littoralis (Boisd.). Seasonal abundance of the insect population and predication of field generation throw a light on the temperature influence on insect development in the field. The data obtained in this work showed that the cotton leafworm, S. littoralis had four generations on sugar beet during the period from September 1st to March 1st. The predicted peaks of generations could be detected when the accumulated thermal units reach 524.27 degree days (dd's). The predicted peaks for the four generations detected earlier or later +3 to -2 days than the observed peaks. The expected peaks and the corresponding expected generations for cotton leafworm could be helpful to design the IPM control program

Early detection of the Mediterranean Fruit Fly, Ceratitis capitata (Wied.) in oranges using different aspects of remote sensing applications

Mediterranean Fruit Fly, Ceratitis capitata (Diptera: Tephritidae) is regarded as an important pest of orange (Citrus). Early detection of pest infestations enables the optimal application of preventative and control measures. This study was carried out under laboratory conditions, in order to predict and monitor orange pest infestations. Consequently, the scope was to find a remote sensing application that can help in the prediction of Mediterranean Fruit Fly infestation in oranges with the least loss in production. Spectroscopic and thermal imaging techniques were investigated, as effective tools in determination of pest infestation and damage in orange fruits. According to the findings, the optimum spectral zones that can be used to discriminate and differentiate between healthy (non-infected) orange fruit and infected ones were red and near infrared bands. Six vegetation indices were calculated to analyze the Field Spectral measurements. By calculating the NPCI (Normalized Pigment Chlorophyll Index), it was found that NPCI values for infected orange fruits were higher in comparison to healthy ones. Thermal imaging showed that the infected orange fruit temperatures were on average 0.8 °C higher than that of healthy fruits. As the maximum temperature differential (MTD) between healthy and infected fruits were 23.7–24.5 °C, respectively. These spectral reflectance curves were useful for researchers working on Site-specific crop management, as they can use remote sensing to detect individual fruit infections. Also, this technique should be used as a powerful and non-destructive method for assistance in agriculture

Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article)

Pre-harvest prediction of a crop yield may prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. Remote sensing has numerous returns in the area of crop monitoring and yield prediction which are closely related to differences in soil, climate, and any biophysical and biochemical changes. Different remote techniques could be used for crop monitoring and yield prediction including multi and hyper spectral data, radar and lidar imagery. This study reviews the potentialities, advantages and disadvantages of each technique and the applicability of these techniques under different agricultural conditions. It also shows the different methods in which these techniques could be used efficiently. In addition, the study expects future scenarios of remote sensing applications in vegetation monitoring and the ways to overcome any obstacles that may face this work. It was found that using satellite data with high spatial resolution are still the most powerful method to be used for crop monitoring and to monitor crop parameters. Assessment of crop spectroscopic parameters through field or laboratory devices could be used to identify and quantify many crop biochemical and biophysical parameters. They could be also used as early indicators of plant infections; however, these techniques are not efficient for crop monitoring over large areas