Force and power requirement for development of cumin harvester: a dynamic approach.

An experimental setup was developed for simulating the field conditions to determine the force and power required for cutting cumin crops in dynamic conditions. The effect of cutter bar speeds, forward speeds, and blade type on cutting force and power requirement for cutting cumin were also studied. Experiments were carried out at three levels: cutter bar speeds, forward speeds, and blade type. The results showed that all the factors significantly affected cutting force. The cutting force followed a decreasing trend with the increase in cutter bar speed. Whereas it followed an increasing trend with the increase in forward speed. The maximum cutting force for all three blades was observed at a cutter bar speed of 2.00 strokes.s-1 and forward speed of 0.46 m.s-1. The idle power and actual power required for cutting the cumin crop were also determined based on the cutting force. The results obtained were validated by the power drawn from the power source while operating the cutter bar blades. The R2 values for Blade-B1, Blade-B2, and Blade-B3 were 0.90, 0.82, and 0.88, respectively. The cutting force was primarily affected by the cutter bar speed, resulting in PCR values of 74.20%, 82.32%, and 81.75% for Blade-B1, Blade-B2, and Blade-B3, respectively, followed by the forward speed, which also had an impact on PCR values of 16.60%, 15.27%, and 18.25% for Blade-B1, Blade-B2, and Blade-B3, respectively. The cutting force for Blade-B1, Blade-B2, and Blade-B3 varied from 15.96 to 58.97 N, 21.08 to 76.64 N, and 30.22 to 85.31, respectively, for the selected range of cutter bar speed and forward speed. Blade-B1 had 18 and 30% less power consumption than Blade-B2 and Blade-B3, respectively.

T-YOLO: a lightweight and efficient detection model for nutrient buds in complex tea-plantation environments.

BACKGROUND: Quick and accurate detection of nutrient buds is essential for yield prediction and field management in tea plantations. However, the complexity of tea plantation environments and the similarity in color between nutrient buds and older leaves make the location of tea nutrient buds challenging. RESULTS: This research presents a lightweight and efficient detection model, T-YOLO, for the accurate detection of tea nutrient buds in unstructured environments. First, a lightweight module, C2fG2, and an efficient feature extraction module, DBS, are introduced into the backbone and neck of the YOLOv5 baseline model. Second, the head network of the model is pruned to achieve further lightweighting. Finally, the dynamic detection head is integrated to mitigate the feature loss caused by lightweighting. The experimental data show that T-YOLO achieves a mean average precision (mAP) of 84.1%, the total number of parameters for model training (Params) is 11.26 million (M), and the number of floating-point operations (FLOPs) is 17.2 Giga (G). Compared with the baseline YOLOv5 model, T-YOLO reduces Params by 47% and lowers FLOPs by 65%. T-YOLO also outperforms the existing optimal detection YOLOv8 model by 7.5% in terms of mAP. CONCLUSION: The T-YOLO model proposed in this study performs well in detecting small tea nutrient buds. It provides a decision-making basis for tea farmers to manage smart tea gardens. The T-YOLO model outperforms mainstream detection models on the public dataset, Global Wheat Head Detection (GWHD), which offers a reference for the construction of lightweight and efficient detection models for other small target crops. © 2024 Society of Chemical Industry.

Correlation between vegetation indexes generated at Vitis Vinifera L. and soil, plant and production parameters for emergency application in decision making / Correlação entre indices de vegetação gerados em Vitis Vinifera L. e parâmetros de solo, planta e produção para aplicação emergencial na tomada de decisão

Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.

Avaliou-se a correlação entre as técnicas de sensoriamento proximal e os resultados laboratoriais de variáveis qualitativas, mais os atributos agronômicos do solo de um vinhedo de 3,0 ha no município de Muitos Capões, região nordeste do estado do Rio Grande do Sul, Brasil, na safra 2017/2018. Objetivou avaliar a substituição das análises laboratoriais convencionais em viticultura por Índices de Vegetação, em situações de indisponibilidade de acesso ao laboratório. Com base em pesquisa bibliográfica, buscaram-se índices vegetativos desenvolvidos ou utilizados para análise de refletância de dossel em videiras e cujas bandas de trabalho estavam dentro do intervalo espectral fornecido pelo equipamento utilizado, obtendo-se um total de 17 candidatos viáveis. Esses índices de vegetação escolhidos foram correlacionados, por meio de Pearson (5%), com atributos agronômicos do solo (condutividade elétrica aparente, argila, pH em H2O, fósforo, potássio, matéria orgânica, alumínio, cálcio, magnésio, CTC efetivo, CTC em pH 7,0, zinco, cobre, enxofre e boro) para profundidades de 0 - 20 cm e 20 - 40 cm, e tecido vegetal (nitrogênio, fósforo, potássio, cálcio, magnésio, enxofre, cobre, zinco, ferro, manganês e boro), além de alguns parâmetros enológicos e fitotécnicos essenciais para a quantificação da produção e qualidade do vinho. Deste cruzamento foram obtidas 139 correlações significativas, resultando 36 coeficientes moderados entre 19 variáveis de parâmetros versus 12 dos índices. Concluímos que nos casos em que o acesso ou disponibilidade de análises laboratoriais é difícil ou impraticável, a utilização de índices de vegetação é possível, desde que os coeficientes de correlação atinjam, pelo menos, a magnitude moderada, servindo como suporte para a tomada de decisão até a falta de estrutura analítica ser remediada.

Pleurotus spp. cultivation on Brachiaria sp. straw treatment with alkaline water

Abstract The aim of this research was to evaluate the efficiency of aqueous alkali-treated Brachiaria straw for the cultivation of appropriate species of oyster mushroom. The substrate used in the cultivation of various Pleurotus spp. was soaked for 20 min by using two different procedures: (i) 0.5-2.0% Ca(OH)2 in 100 L water, and (ii) 50-250 L water. As a result, 1% Ca(OH)2 dissolved in 100 L water and 3.5 kg of Brachiaria straw presented the best production. The most suitable species for the application of the present method were P. pulmonarius and P. sapidus. The success of this technique is directly related to the concentration of Ca(OH)2 and water, the species, and the origin and quality of raw material used as the substrate in the production of oyster mushroom.