Recent years have witnessed enormous growth in Unmanned Aircraft System (UAS) and sensor technology which made it possible to collect high spatial and temporal resolutions data over the crops throughout the growing season. The objective of this research is to develop a novel machine learning framework for marketable tomato yield estimation using multi-source and spatio-temporal remote sensing data collected from UAS. The proposed machine learning model is based on Artificial Neural Network (ANN) and it takes UAS based multi-temporal features such as canopy cover, canopy height, canopy volume, Excessive Greenness Index along with weather information such as humidity, precipitation, temperature, solar radiations and crop evapotranspiration (ETc) as input and predicts the corresponding marketable yield. The predicted yield is validated using the actual harvested yield. Breeders may be able to use the predicted yield as a parameter for genotype selection so that they can not only increase their experiment size for faster genotype selection but also to make efficient and informed decision on best performing genotypes. Moreover, yield prediction maps can be used to develop within-field management zones to optimize field management practices.
Tomato production faces constant pressure of biotic and abiotic stresses that can cause significant loss of production and fruit quality. In tropical and subtropical climates, the main disease affecting tomato production is caused by Tomato Yellow Leaf Curl Virus (TYLCV), a virus that is vectored by the silverleaf whitefly (Bemisia tabaci). The main method of control relies on insecticide spray to control the vector, avoiding the spread of the disease. Detecting and spatially locating infected plants are required to prevent and control epidemic outbreak of TYLCV. In this study, we aim to develop an unmanned aircraft system (UAS) based TYLCV detection algorithm that can identify affected plants and provide physiological information of the affected plants. Multi-temporal phenotypic attributes, e.g., canopy height, canopy cover, canopy volume, and vegetation indexes including normalized difference vegetation indexes (NDVI), soil adjusted vegetation index (SAVI), and excess green index (ExG) were extracted from the UAS image data. The field experiment was conducted at Texas A and M Agrilife Research and Extension Center at Weslaco, TX. A total of 16 tomato hybrids with different levels of TYLCV resistance were inoculated with viruliferous insects and randomly transplanted in open field with triplicates plots containing 4 plants. One control plot for each tomato hybrid with non-inoculated plants were also planted for validation. Machine learning techniques based on artificial neural networks were used to detect TYLCV symptoms in plants from UAS-driven parameters, and all the plants were tested by polymerase chain reaction (PCR) using specific primers to confirm TYLCV infection. To evaluate how early and accurately the algorithm can detect TYLCV symptoms in tomato plants, various detection models were developed by changing the period of input UAS data. We expect that the suggested system to be a useful framework for monitoring outbreak of TYLCV in large scales, giving the ability for the grower to determine the best time and location to start the vector control and also generate time series physiological data for better understanding of the disease progression.
Unmanned Aerial System (UAS) is becoming a popular choice when acquiring fine spatial resolution images for precision agriculture applications. Compared to other remote sensing data collection platforms, UAS can acquire image data at relatively lower cost with finer spatial resolution with more flexible schedule. In recent years, multispectral sensors that can capture near infrared (NIR) and red edge spectral reflectance have been successfully integrated with UAS, and it is offering more versatility in soil and field analysis, crop monitoring, and plant health assessment. In this study, we aim to investigate the capability of UAS-based crop monitoring system to determine the best management practices for 3 different tomato varieties comparing different planting dates, plant density, use of plastic mulch and fertilization rate. The field and UAS data were acquired during Spring 2016, 2017, and 2018 located in Weslaco, TX. To compare the effect of various treatments in cropping systems, physiological parameters and vegetation indexes (Canopy Cover, Canopy Height, Canopy Volume and Excess Greenness) were extracted from red, green, blue (RGB) data and correlated with final yield data to evaluate practice/treatment to maximize tomato yield. During Spring 2016, we observed highest yield from the early March planting date using white plastic mulch. The results also indicated that the variety yielded higher presented a slow canopy decay towards the end of the season. In Spring 2017, there were differences in yield among the three tomato varieties depending on the fertilization rate, DRP-8551 performed better at low nitrogen level, Mykonos performed better on the two higher nitrogen rates and TAM-Hot-Ty had no significant difference among treatments. Finally, during Spring 2018, it was observed that early March produced the best yields and varieties that were able to slow canopy decay towards the end of season performed better. No significant difference was observed between plant density. It is expected that proposed system can be used to collect reliable data and develop variety and environment specific management practices to increase marketable yield and reduce production cost.