Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.

Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L- and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C- and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects.

This article presents a novel method for predicting the sugarcane harvesting date and productivity using a three-band imaging radar. Taking advantage of working with a multi-band radar, this system was employed to estimate the above-ground biomass (AGB), achieving a root-mean-square error (RMSE) of 2 kg m⁻² in sugarcane crops, which is an unprecedented result compared with other works based on the Synthetic Aperture Radar (SAR) system. By correlating the field measurements of the ripening index (RI) with the AGB measurements by radar, an indirect estimate of the RI by the radar is obtained. It is observed that the AGB reaches its maximum approximately 280 days after planting and the maximum RI, which defines the harvesting date, approximately 360 days after planting for the species IACSP97-4039. Starting from an AGB map collected by the radar, it is then possible to predict the harvesting date and the corresponding productivity with competitive average errors of 8 days and 10.7%, respectively, with three months in advance, whereas typical methods employed on a test site achieve an average error of 30 days with three months in advance. To the best of our knowledge, it is the first time that a multi-band radar is employed for productivity prediction in sugarcane crops.

The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.

A aplicação de técnicas de automação e controle em casas de vegetação teve início nos anos 50, com o controle da temperatura através de termostatos; desde então, as mudanças tecnológicas têm abarcado todos os aspectos que compõem o sistema, do projeto estrutural até aspectos relacionados à sustentabilidade. Dentre os parâmetros que devem ser controlados, a temperatura e umidade relativa do ar, a radiação solar, a concentração de CO2, a ventilação e a fertigação têm tido uma evolução significativa, dos quais se apresenta uma panorâmica através da revisão do estado da arte de trabalhos publicados nessas temáticas, nos últimos anos. Na atualidade, temas relacionados com a redução do impacto ao meio ambiente e a produção eficiente e com qualidade, estão levando as casas de vegetação ao caminho da Agricultura de Precisão.