Brazil is the largest coffee producer in the world. However, it has been a challenge to manage the main pest affecting the plant’s foliar part, the Coffee Leaf Miner (CLM) Leucoptera coffeella (Lepidoptera: Lyonetiidae). To mitigate this, remote sensing has been employed to spectrally characterize various stresses on coffee trees. This study establishes the groundwork for efficient pest detection by investigating the spectral characteristics of CLM infestation at different levels. This research aims to characterize the spectral signature of leaves at different CLM levels of infestation and identify the optimal spectral regions for discriminating these levels. To achieve this, hyperspectral reflectance measurements were made of healthy and infested leaves, and the classes of infested leaves were grouped into minimally, moderately, and severely infested. As the infestation level rises, the 700 nm region becomes increasingly suitable for distinguishing between infestation levels, with the visible region also proving significant, particularly during severe infestations. Reflectance thresholds established in this study provide a foundation for agronomic references related to CLM. These findings lay the essential groundwork for enhancing monitoring and early detection systems and underscore the value of terrestrial hyperspectral data for developing sustainable pest management strategies in coffee crops.

Brazil is the largest producer of oranges in the world and the automatic detection of fruits has been a challenging task in the context of remote sensing, due to variations in fruit appearance, changes in lighting and occlusions of foliage and neighboring fruits. In this sense, this paper focus on the detection of oranges in multispectral images, with different spectral bands and exposures, using a convolutional neural network (CNN) known as YOU ONLY LOOK ONCE (YOLO). The results indicate that, after 300 epochs, the model demonstrated an accuracy of 81.5% and an approximate recovery rate of 85%. Shutter speeds 1/640s and 1/250s are not suitable for detection due to low light and overexposure, respectively. Intermediate values may be more suitable for identifying a larger number of fruits.

Leaf Water Potential (LWP) is an indicator widely used to understand water relations in a coffee tree. Monitoring water potential is a challenge for remote sensing using low-cost multispectral cameras, with images taken by remotely piloted aircraft. The objective of this work was to evaluate the potential of a low-cost camera to discriminate different water treatments in the coffee tree. In addition, the accuracy of models to estimate LWP in the coffee crop was evaluated. The results showed that the NDVI (Normalized Difference Vegetation Index) vegetation index was able to discriminate 61.6 % more plots in a drought regime than the Near-InfraRed (NIR) band in the rainfall regime. For LWP, the architecture that presented the best performance in the detection of water stress was for the first flight (SMOreg algorithm using as predictor variables all bands, Red, Green, and NIR, and the NDVI vegetation index) with RMSE value of 0.1880 and RMSE% of 34.18. For the second flight (Random Tree algorithm, using as predictor variables all bands and NDVI) with RMSE (0.0520) and RMSE% (32.00) values.

Brazil, the world's largest coffee producer, faces challenges managing the coffee leaf miner (Leucoptera coffeella), a significant pest. This study suggests remote sensing for pest control decisions. Two experimental areas in the Cerrado region of Minas Gerais State were analyzed to spectrally characterize infested plants and estimate the number of mines per plant. Results show the ability to differentiate infested plants with greater reflectance variance in the near infrared at 850nm. The performances of the three machine learning algorithms were compared. Determining the number of mines in the group of most infested plants demonstrated slightly higher precision, achieving an RMSE of 22.69% using the Support Vector Machine algorithm. Conversely, the group of least-infested plants obtained the best result with the Random Forest algorithm, achieving an RMSE of 32.47%. These promising results indicated that CLM can be detected using aerial multispectral imaging data.

Brazil plays a crucial role in the global economy due to its significant contribution to the agricultural sector, particularly in coffee production, where it stands out as the largest producer and exporter of processed coffee. Various disturbances can influence coffee plants, causing abnormalities that can hinder their successful growth. Parameters such as plant height and canopy diameter play an essential role in assessing the health and productivity of the plants, reflecting their growth, development, and ability to capture sunlight. Additionally, height is also related to the balanced distribution of nutrients and water, providing valuable information about overall performance and the capacity for healthy production. In this regard, the application of methodologies involving remote sensing and machine learning algorithms has shown promising results in the rapid and safe acquisition of information about agricultural systems. This study evaluates different machine learning algorithms, using radiometric values from multispectral images obtained by remote sensing platforms as input datasets for estimating plant height and canopy diameter in coffee cultivation. The best performance was observed for architectures that showed lower RMSE and RMSE% values. For the plant height parameter (m), the RGB sensor exhibited the best performance using the Random Tree algorithm, with an RMSE (0.27) and RMSE% (8.80). For the canopy diameter (m), the sensor showed the best performance using the Random Forest algorithm, with an RMSE (0.15) and RMSE% (8.16).