Artificial Intelligence (AI) has changed how processes are developed, and decisions are made in the agricultural area replacing manual and repetitive processes with automated and more efficient ones. This study presents the application of deep learning techniques to detect and segment weeds in agricultural crops by applying models with different architectures in the analysis of images captured by an Unmanned Aerial Vehicle (UAV). This study contributes to the computer vision field by comparing the performance of the You Only Look Once (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), Mask R-CNN (with framework Detectron2), and U-Net models, making public the dataset with aerial images of soybeans and beans. The models were trained using a dataset consisting of 3021 images, randomly divided into test, validation, and training sets, which were annotated, resized, and increased using the Roboflow application interface. Evaluation metrics were used, which included training efficiency (mAP50 and mAP50-90), precision, accuracy, and recall in the model’s evaluation and comparison. The YOLOv8s variant achieved higher performance with an mAP50 of 97%, precision of 99.7%, and recall of 99% when compared to the other models. The data from this manuscript show that deep learning models can generate efficient results for automatic weed detection when trained with a well-labeled and large set. Furthermore, this study demonstrated the great potential of using advanced object segmentation algorithms in detecting weeds in soybean and bean crops.

Precision irrigation is essential to improve water use efficiency (WUE), defined as the amount of biomass produced per unit of water used by plants. Our objective is to evaluate the effect of different soil volumetric water content (VWC) in plant growth, fruit yield, quality, and WUE of cherry tomatoes grown in a greenhouse. We tested four VWC thresholds (0.23, 0.30, 0.37, and 0.44 m³ m⁻³) to trigger a drip irrigation system in two tomato cultivars (‘Sweet Heaven’ and ‘Mascot F1’). The experiment was arranged in a split-plot design with four replications. We used capacitance sensors connected to an open-source, low-cost platform to monitor and control the irrigation in real-time based on demand. Plants were watered every time the soil VWC dropped below the set thresholds. The treatment with VWC 0.44 m³ m⁻³ resulted in the highest fruit yield, with 102.10% higher WUE when compared to the VWC 0.23 m³ m⁻³ in both cultivars. Fruit quality traits such as longitudinal and equatorial diameter increased asymptotically with soil water content. In contrast, treatments with deficit irrigation increased the fruit soluble solids by 15.73% in both cultivars. These results strongly suggest that accurate control of the soil VWC is essential to modulate the fruit yield and quality attributes in tomatoes produced in the greenhouse.

Integrating advanced technologies such as artificial intelligence (AI) with traditional agricultural practices has changed how activities are developed in agriculture, with the aim of automating manual processes and improving the efficiency and quality of farming decisions. With the advent of deep learning models such as convolutional neural network (CNN) and You Only Look Once (YOLO), many studies have emerged given the need to develop solutions to problems and take advantage of all the potential that this technology has to offer. This systematic literature review aims to present an in-depth investigation of the application of AI in supporting the management of weeds, plant nutrition, water, pests, and diseases. This systematic review was conducted using the PRISMA methodology and guidelines. Data from different papers indicated that the main research interests comprise five groups: (a) type of agronomic problems; (b) type of sensor; (c) dataset treatment; (d) evaluation metrics and quantification; and (e) AI technique. The inclusion (I) and exclusion (E) criteria adopted in this study included: (I1) articles that obtained AI techniques for agricultural analysis; (I2) complete articles written in English; (I3) articles from specialized scientific journals; (E1) articles that did not describe the type of agrarian analysis used; (E2) articles that did not specify the AI technique used and that were incomplete or abstract; (E3) articles that did not present substantial experimental results. The articles were searched on the official pages of the main scientific bases: ACM, IEEE, ScienceDirect, MDPI, and Web of Science. The papers were categorized and grouped to show the main contributions of the literature to support agricultural decisions using AI. This study found that AI methods perform better in supporting weed detection, classification of plant diseases, and estimation of agricultural yield in crops when using images captured by Unmanned Aerial Vehicles (UAVs). Furthermore, CNN and YOLO, as well as their variations, present the best results for all groups presented. This review also points out the limitations and potential challenges when working with deep machine learning models, aiming to contribute to knowledge systematization and to benefit researchers and professionals regarding AI applications in mitigating agronomic problems.

Drying air temperature has direct influence on seed quality, especially for vegetable species harvested with high moisture content. This study aimed to determine and model the drying curves of ‘Cabacinha’ pepper seeds (Capsicum chinense L.) at different temperatures, and to evaluate their physiological quality after drying and during storage. The experimental design was completely randomized in a 3 x 3 factorial scheme, with three drying temperatures (35, 38 and 42 °C) and three storage periods (0, 4 and 8 months), with four replicates. Seed quality was evaluated by germination test, first germination count, accelerated aging, electrical conductivity, seedling emergence, and speed of emergence. The Two Terms is the model that best represents the drying curves of ‘Cabacinha’ pepper seeds at temperatures of 35, 38 and 42 °C. Higher germination and vigor values are obtained in seeds dried at 35 and 38 °C. Drying at 42 °C and storage reduce the physiological quality of the seeds.

Drying is one of the most important steps of the post-harvest of pepper seeds and must be performed carefully so as not to impair the quality of seeds. The study of thermodynamic properties provides information about the behavior of water in agriculture products and the energy requirement for removal thereof during the drying pro­cess. Based on the above, this study aimed to determine and evaluate the thermodynamic properties for different equilibrium moisture contents in pepper seeds of the variety ‘Cabacinha’. Seeds with an initial moisture content of 22.7% on dry basis (d.b.) were subjected to drying at 40 °C, until reaching moisture content of 3.9% (d.b.). Based on the obtained results, it is concluded that the integral isosteric heat of desorption, the Gibbs free energy, the differential enthalpy and entropy increase with the reduction of the moisture content of pepper seeds. The Gibbs free energy showed positive values, indicating that the process of desorption is not spontaneous. The theory of the enthalpy-entropy compensation can be applied to the desorption process, since this is controlled by enthalpy.

Os sistemas agroflorestais (SAFs) quando implantados e conduzidos sob os princípios da agroecologia e com o manejo adequado possibilitam acelerar os processos de regeneração de sistemas ambientais, como na recuperação de áreas degradadas e na produção de alimentos. Neste sentido, objetivou-se com este projeto restaurar uma área improdutiva com a implantação de um sistema agroflorestal em uma propriedade de agricultura familiar no município de Ceres, GO. A metodologia utilizada foi a implantação de um SAF in loco, com participação direta do agricultor e de alunos do IF Goiano – Campus Ceres. No sistema foram introduzidas olerícolas e cultura anual, fruteiras e plantas arbóreas. Após um ano de implantação, o SAF proporcionou incremento no potencial produtivo da área da chácara São Pedro. É uma opção para tornar-se áreas degradadas e/ou não utilizadas/improdutivas em uma área com produção de alimentos, preservação ambiental e promoção do bem estar para as famílias do campo.